44 results about "Nonlinear neural networks" patented technology

A system and method for fault detection is provided. The fault detection system provides the ability to detect symptoms of fault in turbine engines and other mechanical systems that have nonlinear relationships. The fault detection system uses a neural network to perform a data representation and feature extraction where the extracted features are analogous to principal components derived in a principal component analysis. This neural network data representation analysis can then be used to determine the likelihood of a fault in the system.

A neural network classifier provides the ability to separate and categorize multiple arbitrary and previously unknown audio sources down-mixed to a single monophonic audio signal. This is accomplished by breaking the monophonic audio signal into baseline frames (possibly overlapping), windowing the frames, extracting a number of descriptive features in each frame, and employing a pre-trained nonlinear neural network as a classifier. Each neural network output manifests the presence of a pre-determined type of audio source in each baseline frame of the monophonic audio signal. The neural network classifier is well suited to address widely changing parameters of the signal and sources, time and frequency domain overlapping of the sources, and reverberation and occlusions in real-life signals. The classifier outputs can be used as a front-end to create multiple audio channels for a source separation algorithm (e.g., ICA) or as parameters in a post-processing algorithm (e.g. categorize music, track sources, generate audio indexes for the purposes of navigation, re-mixing, security and surveillance, telephone and wireless communications, and teleconferencing).

Neural networks provide efficient, robust and precise filtering techniques for compensating linear and non-linear distortion of an audio transducer such as a speaker, amplified broadcast antenna or perhaps a microphone. These techniques include both a method of characterizing the audio transducer to compute the inverse transfer functions and a method of implementing those inverse transfer functions for reproduction. The inverse transfer functions are preferably extracted using time domain calculations such as provided by linear and non-linear neural networks, which more accurately represent the properties of audio signals and the audio transducer than conventional frequency domain or modeling based approaches. Although the preferred approach is to compensate for both linear and non-linear distortion, the neural network filtering techniques may be applied independently.

Methods and apparatus are provided pertaining to a design of experiments. The method comprises generating a data set from historical data; identifying and removing any fault data points in the data set so as to create a revised data set; supplying the data points from the revised data set into a nonlinear neural network model; and deriving a simulator model characterizing a relationship between the input variables and the output variables. The apparatus comprises means for generating a data set from historical data; means for identifying and removing any fault data points in the data set so as to create a revised data set; means for supplying the data points from the revised data set into a nonlinear neural network model; and means for deriving a simulator model characterizing a relationship between the input variables and the output variables.

Methods and apparatus are provided for determining refrigerant charge in a vapor compressor system (VCS) of an aircraft. The methods and apparatus comprise the following steps of, and / or means for, generating a data set from historical data representative of a plurality of VCS operating conditions over time, identifying one or more steady-state data points in the generated data set, forming a revised data set that includes at least the steady-state data points, using principal components analysis (PCA) to derive values for a plurality of minimally correlated input variables, supplying the derived values for the plurality of minimally correlated input variables and the corresponding values for the VCS refrigerant charge in the revised data set to a nonlinear neural network model, and deriving a simulator model characterizing a relationship between the plurality of minimally correlated input variables and the VCS refrigerant charge.

The invention provides a method for fusing a full-color image and a multispectral image based on a deep neural network. Specific steps are as follows: step 1. constructing a training set of high resolution and low resolution image block pairs; step 2. using an improved sparse denoising self-encoder to learn to train initialization parameters of a first layer in a neural network model; step 3. using the improved sparse denoising self-encoder to perform pretraining of the neural network layer by layer; step 4. performing fine adjustment of parameters of the pretrained deep neural network; and step 5. using the deep neural network to reconstruct a multispectral image of high resolution according to a known multispectral image of low spatial resolution. The method provided by the invention adopts a method of deep learning, and can make full use of a nonlinear neural network to depict complex structural information of a multispectral image, thereby enabling the fused multispectral image to have high spatial resolution, and well keeping spectral information of the multispectral image.

The application provides a crack seismic attribute judging system and a crack seismic attribute judging method. The method comprises the following steps: identifying and calculating out crack information of single-well crack density according to acquired imaging logging data and core analysis data, and generating a single-well crack model; extracting seismic attribute data of a specified type from acquired seismic data, and selecting an optimal seismic attribute of which the correlation with the reservoir meets a preset requirement; building a training model by taking the single-well crack model as an input signal and the optimal seismic attribute as an output signal; using an artificial intelligence nonlinear neural network to train the training model; and when the optimal seismic attribute in the training model meets a set screening condition, outputting the optimal seismic attribute meeting the set screening condition. According to the embodiments of the application, the advantages and disadvantages of numerous seismic attributes can be identified intelligently, quickly and accurately, well detection cracks can be identified effectively based on seismic attributes, and the effect and efficiency of actual operation are improved greatly.

The invention provides an optimization control method in the batch reaction process of a batch reactor. The method includes the following steps of S100, calculating the optimal value track of a control object through a TLBO algorithm according to a received economic optimization index to serve as the reference track of a control layer; S200, ensuring the boundedness of input and output signals of a closed-loop system through a linear generalized predictive controller in the batch reaction process, compensating for a nonlinear term of the system through a nonlinear neural network generalized predictive controller, and selecting the linear generalized predictive controller or the nonlinear neural network generalized predictive controller according to a preset switching performance index at each sampling moment so that the optimal value track obtained in the step S100 can be tracked by a controlled object; S300, restraining and eliminating the influences of disturbance entering the system on output through a bottom layer controller so as to output the value of the control object.

The invention relates to a method for the optimization of operation and control of energy conservation for a reserve ball-grinding powder system. Firstly, a non-linear neural network model about the consumption of powder of a powder-making system and operating parameters is established through collecting the service data of the powder-making system, then the negative pressure and the temperature at the entrance of a coal mill and the temperature at the outlet of the coal mill are obtained through the non-linear optimization with restricted conditions as a set value for controlling the system,wherein, the negative pressure, the temperature at the entrance and the outlet lead to lower consumption of powder; the entire optimized controlling of the powder-making system is carried out throughthe intelligent control of rotation of the coal mill and multivariable decoupling control of parameters at the entrance of the coal mill based on the operating experience as well as an intelligent down draft control technology, so as to ensure that the powder-making system strictly runs at an optimal condition and effectively reduces the powder consumption. The method provided by the invention caneffectively inhibit the fluctuations of a primary air pressure in the on / off process of the powder-making system, put an end to serious accidents such as flameout in a boiler caused by down draft andeffectively reduce the operating intensity for the staff.