546 results about "Multi layered perceptron" patented technology

The invention belongs to the field of hollowing detection, and provides an outer wall hollowing detection method and system, and the method comprises the steps: carrying out the multi-mode detection of an outer wall, and obtaining an infrared thermal imaging image and an ultrasonic detection signal; performing image enhancement processing on the infrared thermal imaging image; respectively carrying out feature extraction on the infrared thermal imaging image and the ultrasonic echo image by using a convolutional neural network; extracting and fusing high-dimensional feature vectors, and aligning an infrared thermal imaging image with an ultrasonic echo image through an image registration algorithm and a spatial transformation model; and classifying the fused feature vectors through a multi-layer sensor according to the data aligned in the step, and outputting the specific position, size and severity of the hollowing area. According to the scheme, the outer wall hollowing detection can be efficiently carried out.

A method and apparatus for reconstructing a three-dimensional (3D) scene from a two-dimensional (2D) input image of the scene using a fully-differentiable transformer-based encoder-decode. A 2D input image encoded into a set of image features using a pre-trained vision transformer model, wherein the vision transformer model is pre-trained with multi-view RGB image supervision and point cloud supervision. The set of image features is projected onto a 3D triplane representation using a transformer decoder to obtain output triplane tokens. A triplane representation is created from the tokens and queried. 3D point features of color and density for volumetric rendering re predicted using a multi-layer perceptron. The geometry of the generated 3D asset is represented with a surface mesh including vertices and triangular faces. A texture map by is created with a multichannel image in UV space. Multiple views of the 3D scene are simultaneously generated based on the surface mesh.

The invention discloses a double-flow time convolution enhanced interactive bearing life prediction method, which comprises the following steps of: acquiring original bearing vibration signal data, and processing and reconstructing the data; constructing a time flow feature extraction module by fusing the local time sequence modeling capability of the TCN and the multi-scale expansion attention, dynamically adjusting the expansion rate through the spectrum entropy, and capturing multi-scale local features by combining sparse multi-head attention; a spatial stream feature extraction module is constructed by fusing frequency sensing position coding and a layered sparse attention mechanism; designing a bidirectional cross-layer attention collaboration mechanism, performing feature interaction on the time flow feature extraction module and the spatial flow feature extraction module, enhancing the degradation characterization capability through hierarchical feature alignment and dynamic weight adjustment, and constructing a double-flow feature extraction layer; inputting the reconstructed signal into a double-flow feature extraction layer to extract features; inputting the extracted features into a multi-layer sensor to carry out RUL prediction; local-global feature complementation is realized; and prediction stability is improved.

The invention discloses a structure perception graph neural network physical field prediction method based on a geometric gating attention mechanism, and belongs to the technical field of artificial intelligence and computational physics crossing. The method comprises the following steps: extracting geometric features for standardization processing and organizing in a graph data structure; inputting the edge-level geometric features into a geometric gating function, generating a dynamic adjustment factor through a multi-layer perceptron, obtaining an attention weight after geometric modulation, and performing normalization processing to obtain output features of nodes; splicing the output features of the plurality of attention heads as final output features of the model; training the graph neural network model to obtain a physical field prediction model; and inputting the geometric features of the three-dimensional structure model to be predicted into the physical field prediction model. According to the method, the precision and physical consistency of complex physical field prediction are improved, and the problems of boundary blur, field intensity sudden change and the like caused by neglecting geometric dynamic change in a traditional static coding method are reduced.

The invention relates to the technical field of intelligent medical systems, and discloses a cerebral apoplexy onset risk assessment and reminding method and system.The method comprises the steps that continuous medical structured detection data are collected, and the data comprise carotid artery blood flow parameters, brain oxygen saturation, heart rate variability and metabolic indexes; inputting a bidirectional LSTM, a differential convolutional network, a wavelet residual network and a multi-layer perceptron to extract nonlinear features; constructing a neural function coupling structure diagram of four nodes of cerebral blood supply, oxygen supply, autonomous regulation and metabolic steady state; calculating inter-node time sequence offset correlation and a stable factor to obtain a coupling anomaly coefficient; and driving the embedded network by using a graph structure and a node feature input mechanism, and outputting a risk state assessment result. According to the method, the neural function coupling structure diagram is constructed and the mechanism is introduced to drive the embedded network, so that high-precision identification of the multi-system collaborative abnormal state and dynamic evaluation of the stroke risk level are realized.

The invention discloses a snake-shaped robot control method based on reinforcement learning. The snake-shaped robot control method comprises the steps that a complex terrain simulation scene comprising walls, cubic obstacles and low channels is built; modeling is conducted on the structure, joints and kinetic parameters of the snake-shaped robot, and a snake-shaped robot simulation model is obtained; defining a state space and an action space of the snakelike robot, and designing a diversified reward function; constructing an action network and a value network by adopting a multi-layer perceptron (MLP) structure; and performing multi-round simulation training on the snakelike robot by applying a reinforcement learning algorithm, so that the robot learns a parameter set for making an optimization decision under different scenes, thereby realizing optimization control of the snakelike robot and reaching a target point. According to the method, visual information does not need to be depended on, efficient and stable motion control of the snake-shaped robot in a complex environment can be achieved through autonomous learning, and the crossing capacity of the snake-shaped robot in a low and narrow space is improved.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

The invention relates to an air conditioner dynamic optimization method and system based on deep learning, and belongs to the technical field of air conditioner intelligent control. The method comprises the steps of obtaining air conditioner operation data, constructing an air conditioner operation data set through air conditioner data preprocessing according to the air conditioner operation data, and obtaining an air conditioner user preference model through multi-layer perceptron training and testing according to the air conditioner operation data set, according to the air conditioner user preference model, an air conditioner user preference DQN model is constructed through an initialized DQN; environment sensor data are collected in real time, a user air conditioner optimal control strategy is output through the air conditioner user preference DQN model according to the environment sensor data, and air conditioner equipment is regulated and controlled in real time through an air conditioner control system according to the user air conditioner optimal control strategy. Therefore, the air conditioner parameters are dynamically optimized based on the user comfort level and the energy-saving target.

The invention provides a lightweight potential diffusion model design method and system based on evolutionary neural architecture search, and the method employs a super-network technology to train a plurality of network architectures at the same time, combines an evolutionary genetic algorithm in an architecture search process, and trains a multi-layer perceptron as a score predictor, so as to improve the performance of the model. According to the method, the optimal architecture is efficiently screened out in a large-scale search space through the assisted evolution genetic algorithm, and the resource consumption and the calculation cost needed in the traditional architecture design process are remarkably reduced. According to the method, the pre-trained teacher model is introduced in combination with the knowledge distillation technology to supervise the training of the student model, and the potential diffusion model can be lightened while the stable performance of the potential diffusion model is ensured. Through the method, an efficient neural network architecture can be automatically designed, efficient deployment of the potential diffusion model can be realized, and a brand-new and low-cost solution is provided for optimization of the diffusion model.

The invention discloses a multi-modal sentiment analysis method based on tensor dynamic interaction and decoupling, which belongs to the field of multi-modal information processing, and comprises the following steps: feature extraction: respectively inputting original modal data into different encoders for feature extraction to generate multi-modal potential feature representation; performing feature interaction and decoupling, expanding and splicing, and generating interaction features by using a Cartesian product and Einstein summation agreement; convolution and pooling: performing convolution operation on the interactive features to gradually extract features with higher dimensions, flattening a feature map into a one-dimensional feature vector, and processing each modal feature by using a Transform encoder; and S4, emotion prediction: inputting the decoupled feature vectors, performing emotion label prediction through a multi-layer perceptron, and optimizing prediction precision through a mean absolute error loss function. According to the method, the defects of an existing multi-modal sentiment analysis model in the aspects of processing inter-modal interaction and feature decoupling can be overcome.

The invention relates to the technical field of electronic detection, and discloses an electronic component dispensing detection method. The method comprises the following steps: acquiring dispensing image data of the surface of the electronic component, and generating a standardized dispensing data matrix containing glue position, thickness and uniformity characteristics through standardized preprocessing; constructing a dispensing correction matrix based on an adaptive window frame, and performing spatial reference dynamic correction on the standardized data matrix to obtain a spatial correction dispensing data matrix; inputting the data into a multi-layer sensor fusion network for feature fusion, and outputting a multi-source fusion dispensing data set; constructing a multi-dimensional abnormal feature incidence matrix based on the data set, and identifying abnormal dispensing data nodes by using a dynamic threshold detection algorithm; performing parameter optimization iteration on the multi-source fusion data set by using a gradient descent optimization algorithm to generate an optimized dispensing parameter set; and finally, constructing a three-dimensional visual dispensing quality model, and establishing a dynamic mapping relationship between model parameters and glue physical characteristics. The method can more comprehensively detect the glue quality.

The invention discloses a single-view three-dimensional dressed human body reconstruction method based on pixel and voxel feature fusion. The method comprises the steps of data set preprocessing, data set dividing, three-dimensional dressed human body reconstruction network construction, three-dimensional dressed human body reconstruction network training, three-dimensional dressed human body reconstruction network testing and reconstruction result displaying. In the pixel feature extraction network, the coordinate attention and the stacking hourglass network are combined, so that the pixel feature extraction precision is improved; in the voxel feature extraction network, a three-dimensional channel and space attention are introduced into the voxelization human body parameterization model network, and a multi-scale residual feature extraction module is combined to extract voxel features. And splicing the extracted pixels and voxel feature vectors, inputting the spliced pixels and voxel feature vectors into a multi-layer perceptron, and reconstructing a three-dimensional dressed human body model through Marking Cubes, thereby solving the technical problem of insufficient precision of the existing single-view three-dimensional dressed human body reconstruction method, and being applicable to the technical fields of artificial intelligence and computer vision.

The invention provides a multi-modal feature extraction network enhanced negotiation decision-making method based on a space-time directed graph, and aims to enhance a negotiation decision-making algorithm of an automatic driving vehicle. According to the method, three graph structures including a view angle graph, a distance graph and a collision time graph are constructed to comprehensively capture and describe the complex dynamic and asymmetric interaction relation between vehicles. Each graph structure is subjected to feature extraction through a graph attention network (GAT), and deep features of different interactive prior information are fully mined. Then, the extracted multi-modal features are fused through a multi-layer perceptron (MLP), and unified feature representation is formed; the comprehensive representation is further input into a reinforcement learning module based on an Actor-Critic framework to be used for optimizing a driving strategy. Through the innovative architecture design, the decision-making ability and the safety performance of the automatic driving system can be remarkably improved in a complex and dynamic traffic environment, and it is ensured that efficient and stable autonomous driving of the vehicle is achieved under the high-density traffic condition.

The invention discloses an oil mist particle trajectory control method and system in oil mist separation, and relates to the technical field of intelligent control, and the method comprises the steps: collecting the three-dimensional position and velocity vector of oil mist particles in real time, and carrying out the three-dimensional modeling of an oil mist trajectory; taking the deviation between the actually measured track and the ideal track as a training sample, inputting the training sample into a multi-layer perceptron, and outputting a current error vector and a compensation increment; constructing a multi-target evaluation function based on the compensated position target and the safe energy consumption factor; optimizing the control parameters by adopting a gray wolf optimization algorithm with improved congestion degree; and issuing the optimized parameters to an execution system to automatically adjust corresponding values. By means of the method, high-precision online control over oil mist particle movement is achieved; the separation efficiency and the energy consumption are effectively balanced; the system has self-adaptive closed-loop capacity, parameters can be quickly adjusted when working conditions change, and separation stability and equipment safety are guaranteed; manual intervention is reduced through automatic execution, and operation reliability and production efficiency are improved.

The invention discloses an encrypted network traffic classification method based on a structured state space dual model in the technical field of network information security, and aims to solve the optimization problem of encrypted network traffic classification. The method comprises the following steps: acquiring a pcap data packet in encrypted network traffic; extracting hierarchical flow information in the flow packet and generating a stride sequence; inputting the stride sequence into a pre-trained encrypted network traffic classification model, and outputting a classification result; the training of the encrypted network traffic classification model comprises the following steps: carrying out self-supervised pre-training on unmarked data by using a mask auto-encoder, and learning universal representation of traffic data; and replacing a model decoder with a multi-layer perceptron head, and performing fine tuning optimization by using mark data. According to the method, the reasoning speed is improved while low GPU memory use is kept, and encrypted traffic classification can be more efficiently and effectively achieved.

The invention discloses a distributed photovoltaic power prediction method and device, equipment, a medium and a product, and relates to the technical field of distributed photovoltaic. The method comprises the steps of obtaining a multi-source meteorological matrix; according to the historical power time sequence data, a fuzzy clustering algorithm based on a time sequence form is adopted to carry out regional division on the distributed photovoltaic power stations in the to-be-predicted region; determining an optimal network parameter of each sub-region by adopting a Bayesian method; applying the optimal network parameters to the Transform model of each sub-region to obtain a distributed photovoltaic power prediction model of each sub-region; and inputting the multi-source meteorological matrix in the future prediction time period into the distributed photovoltaic power prediction model of each region, and obtaining a distributed photovoltaic power prediction result of the region to be predicted in the future prediction time period by using the first multilayer sensor. The distributed photovoltaic power prediction method and device can be suitable for distributed photovoltaic power prediction, and the accuracy of distributed photovoltaic power prediction is improved.

The invention relates to the technical field of food detection, in particular to a food freshness detection system and method based on a gas sensor array, and the system comprises the following steps: the gas sensor array is used for detecting gas information of at least one target volatile organic compound in a target sample and converting the gas information into a target voltage signal; target processing is conducted on the target voltage signal through the signal control module to obtain a first processing signal, and finally the food freshness of the target sample is analyzed through the intelligent mode recognition display system on the basis of the first processing signal and a target principal component analysis and multi-layer sensor fusion model. And obtaining a food freshness detection result of the target sample. Therefore, the problems that the food freshness detection result is inaccurate due to the cross sensitivity of sensors in related technologies, the detection range and sensitivity of a single sensor cannot meet the detection requirements in a complex environment, and the accuracy and reliability of food freshness detection are reduced are solved.

The invention provides an agricultural robot navigation method based on masked residual Transformer quantum reinforcement learning, and the method is composed of a masked residual Transformer (MRT) network model and the Qua-SAC quantum reinforcement learning supporting the model. The input of the MRT is an original RGB image in a target state under polar coordinates and an original RGB image in a visual state, the feature extraction part is mainly composed of a layer normalization operation layer, a mask multi-head self-attention block, a multi-layer sensor block and a full connection layer, and through the design of the MRT network, target key features can be extracted. The problem of low data efficiency caused by decoupling of a sensing module and target information in an agricultural unstructured environment is solved, and scene representation is promoted to be more focused on target related features. Quantum convolution is introduced in the Qua-SAC quantum reinforcement learning, so that the accuracy and the calculation efficiency of Q value estimation are improved to learn an optimal navigation strategy, and the navigation performance of an agricultural robot in an agricultural non-institution environment is improved.

The invention belongs to the technical field of image processing, and relates to a KAN-based space-frequency double-domain implicit guide sampling remote sensing image fusion method, which comprises the steps of constructing a training sample, creating a KAN-based space-frequency double-domain implicit guide sampling network, training the network and inputting an image to be fused into the trained network. And a fused image with high spatial resolution and high spectral resolution after fusion is generated through mapping. The network comprises a coding unit, a space implicit guiding unit, a frequency implicit guiding unit and a decoding unit, the coding unit is composed of parallel KANs and maps hyperspectral and multispectral remote sensing images to a depth potential space, and the space implicit guiding unit and the frequency implicit guiding unit conduct guiding implicit sampling on HSI in the space domain and the frequency domain respectively. And the decoding unit fuses the space-frequency domain sampling features and generates a final fused image. According to the invention, a pure KAN-driven architecture is adopted, the dependence on a multi-layer sensor in the prior art is replaced, and the lightweight degree is relatively high.

The invention relates to the technical field of image prediction, and discloses an emotion prediction method and system based on a KAN network, and a storage medium, and the method comprises the steps: collecting a data set, and carrying out the preprocessing of the data set; constructing an emotion prediction model based on a KAN network, training the emotion prediction model by using the training set, and adjusting parameters of the emotion prediction model by using a cross entropy loss function in the training process to obtain a trained emotion prediction model; evaluating the performance of the trained emotion prediction model by using the verification set; and inputting a to-be-predicted image into the trained emotion prediction model, and outputting an emotion prediction result. According to the method, the Kolmogorov-Arnold network is used for replacing a traditional multi-layer perceptron for image sentiment analysis, the KAN network has remarkable advantages in the aspects of complex relation processing and logical reasoning, the sentiment prediction accuracy can be improved, training data are obtained by combining network data collection and a text generation picture model, and the method is high in practicability and high in practicability. And the workload of manual labeling is effectively reduced.

The invention belongs to the technical field of environmental science, and provides an ozone forecasting method based on multi-dimensional meteorological factors and variables, which comprises the following steps: S1, collecting and sorting the multi-dimensional meteorological factors, and calculating change variables of each element; s2, taking all processed multi-dimensional meteorological factors as forecast factors, and carrying out standardization processing; s3, screening all processed forecasting factors by adopting a stepwise regression method to obtain an optimal forecasting factor combination; s4, performing model training on historical data by adopting a multi-layer perceptron machine learning method according to the screened forecast factors; s5, checking and evaluating the trained perceptron learning model, and comparing and evaluating the trained perceptron learning model with a linear regression model and a radial basis function model; s6, predicting the ozone pollution process in different time periods and different weather backgrounds; according to the invention, the accuracy and timeliness of ozone forecasting can be obviously improved, and more reliable data support is provided for weather and environment management departments.

The invention discloses a method for predicting images in a whole growth period based on crop genotypes of artificial intelligence. The method comprises the steps of collecting crop images, processing the crop images, constructing a multi-layer perceptron neural network and a condition control generative adversarial network for training so as to learn the mapping relation of crop genotypes, phenotypes and images in the whole growth period, obtaining a crop gene-phenotype prediction model and a phenotype-image prediction model, and finally obtaining the crop genotype-phenotype prediction model and the phenotype-image prediction model through the two models. Based on the genotype of a to-be-predicted crop, a visual result of the whole growth period of the crop in an ideal environment under the regulation of the gene is predicted. According to the method, based on an artificial intelligence technology, a multi-dimensional space multi-layer perception network and a condition control generative adversarial network are constructed, and vivid and accurate visual prediction of crops is realized by deeply utilizing mapping rules contained in crop genes, phenotypes and image big data.

The invention relates to the technical field of bioinformatics and computational biology, and provides an anti-cancer drug target screening method and system based on hypergraph-knowledge graph dual-channel, a hypergraph-knowledge graph dual-channel architecture is constructed through a feature extraction module, a self-adaptive multi-modal fusion module dynamically integrates multi-source heterogeneous data, and the anti-cancer drug target screening method and system based on the hypergraph-knowledge graph dual-channel architecture are obtained. The method comprises the following steps: adaptively fusing high-order interaction features and semantic association features of a gene, generating a high-quality negative sample by using a generative adversarial network to optimize a training process, finally optimizing feature representation through a discriminator, and calculating a synthetic lethal probability score between any gene pair. According to the application, firstly, the extracted multi-dimensional features are normalized through the feature extraction module, then the importance of different features is dynamically weighted based on the attention mechanism, finally, the synthesis lethal relationship prediction probability of the gene pair is output through the multi-layer sensor, and under the theoretical framework of the gene synthesis lethal effect, the synthesis lethal effect of the gene pair is predicted. And an innovative solution is provided for anti-cancer drug target screening.

The invention discloses a composite insulator defect detection method and system based on a multi-source signal and VMD decomposition, and the method is characterized in that the method belongs to the technical field of power system and equipment monitoring, and comprises the steps: synchronously collecting an ultrasonic echo signal, a mechanical vibration signal and an infrared thermal image signal of a composite insulator, and carrying out the timestamp alignment; performing CEEMDAN denoising and VMD decomposition on the ultrasonic echo signal in sequence to extract time-frequency domain features, and generating an ultrasonic feature vector; performing frequency domain transformation on the mechanical vibration signal to extract fundamental frequency and harmonic characteristics, and generating a vibration characteristic vector; performing surface temperature abnormal feature extraction on the infrared thermal image signal to generate an infrared auxiliary feature vector; the ultrasonic feature vector, the vibration feature vector and the infrared auxiliary feature vector are spliced into a fusion feature vector, and after dynamic weight distribution, the fusion feature vector is input into a multi-layer perceptron model for defect classification; and analyzing and positioning the internal defect position of the composite insulator by combining the ultrasonic signal propagation path time delay and the vibration mode, and generating a three-dimensional visual report.

The invention discloses a one-dimensional plasma simulation method based on deep learning optimization and related equipment, and the method comprises the steps: firstly constructing a one-dimensional basic model containing specific parameters, and then constructing and training a neural network of a multi-layer sensor (MLP) structure for predicting a potential function; then, a particle simulation method (DL-PIC) based on deep learning optimization is executed. Under the condition that 1000 grid points are included, compared with a traditional particle simulation method (PIC), the simulation time of the DL-PIC neural network is shortened by about 33.37%. According to the invention, complex physical phenomena such as reflection oscillation caused by heat effect and space charges can be effectively processed, and the method has good expansibility. The method is not only suitable for one-dimensional simulation, but also can be popularized to higher-dimension and electromagnetic particle simulation. Therefore, the invention provides an efficient and accurate solution for plasma simulation research, and can be widely applied to the technical field of plasma simulation.

A machine-learned architecture for determining whether an object is relevant to a vehicle's action planning may comprise a convolutional neural network, graph neural network, and / or multi-layer perceptron that may determine a relevance score associated with an object that indicates indicating whether an object is likely to impact operation(s) of a vehicle. In some examples, the machine-learned architecture may use scene information and / or an object track to determine the relevance score.

The invention relates to a short-term power load prediction method based on an SWT and an improved Autoformer model, and the method employs the SWT to carry out the de-noising processing of original load data, effectively reduces the interference of noise on a prediction result, and provides clearer input for the model. An autocorrelation mechanism and a multi-head autocorrelation mechanism are introduced into an Autoformer model, so that the capturing capability of similar sub-processes and long-term dependency relationships in a time sequence is enhanced, and the prediction precision is remarkably improved; meanwhile, a multi-scale window moving average decomposition technology is adopted in a hybrid expert decomposition module MOED, the season component and the trend component of a load sequence are effectively extracted, and the adaptability, prediction accuracy and robustness of the model to complex data are improved; the KAN module replaces a traditional multilayer perceptron MLP, parameter consumption is reduced, the generalization ability of the model is improved, and the prediction performance of the model is further enhanced.

The invention discloses an industrial park net load prediction method and system based on a multilayer perceptron, and the method comprises the following steps: dividing historical industrial park operation data according to an audio frequency collection region, and obtaining region historical operation data corresponding to the audio frequency collection region; performing clustering analysis on the historical operation data of each region based on the audio frequency fluctuation, and obtaining audio frequency-aging correlation according to a clustering result; training by using historical industrial park operation data, historical distributed energy operation data and audio frequency-aging correlation to obtain an industrial park net load prediction model; and obtaining industrial park net load prediction data according to the current industrial park operation data, the current distributed energy operation data, the industrial park load net prediction model and the audio frequency-aging correlation. The method has the beneficial effects that an aging model does not need to be constructed for each industrial device, the overall model structure is simplified, and meanwhile, the prediction efficiency and prediction accuracy of the load of the industrial park are improved.

Described herein are systems, methods, and instrumentalities associated with using a multi-layer perceptron (MLP) neural network to process medical images of an anatomical structure. The processing may include padding an input image in accordance with the training of the MLP neural network, splitting the input image (e.g., the padded input image) into patches of a same size, and processing the patches through the MLP neural network over one or more iterations. During an iteration of the processing, the patches may be processed separately and re-combined into an intermediate image before the intermediate image is shifted to concatenate portions of the image that are derived from different patches. This way, global features of the anatomical structure may be learned and used to improve the quality of the image generated by the MLP neural network, without incurring significant computation or memory costs.

A data channel on an integrated circuit device includes a non-linear equalizer having as inputs digitized samples of signals on the data channel, decoding circuitry configured to determine from outputs of the non-linear equalizer a respective value of each of the signals, and adaptation circuitry configured to adapt parameters of the non-linear equalizer based on respective ones of the value. The non-linear equalizer includes a non-linear filter portion, and a front-end filter portion configured to reduce numbers of the inputs from the digitized samples. The non-linear equalizer may be a neural network equalizer, such as a multi-layer perceptron neural network equalizer, a reduced complexity multi-layer perceptron neural network equalizer, or a radial-basis function neural network equalizer. Alternatively, the non-linear equalizer may include a linear filter and a non-linear activation function, which may be a hyperbolic tangent function.