234 results about "Neural network modeling" patented technology

The invention provides a method and a system for deep nerve translation based on character encoding. A combined nerve network model is established by using an RNN to cover the whole translation process, and translation tasks are directly completed from the perspective of an encoder-decoder framework. The method comprises the following steps: A, word vector generation: performing word segmentation on character-level input data through neural network modeling and generating a word vector; B, language model generation: establishing grammar rules by utilizing the characteristic of memory of the recurrent neural network in time; C, word alignment model generation: obtaining the probability of translating multiple words in a source language statement into target language words; D, output: translating an inputted source language into a target language; E, translation model combination: establishing a deep nerve translation model (RNN-embed) based on character encoding in combination with neural network models in the four steps and accelerating model training by using CPU parallel computation.

The invention discloses a reading understanding model method based on paragraph internal reasoning and joint question answer matching, and the method comprises the following steps: S1, constructing avector for each candidate answer, the vector representing the interaction of a paragraph with a question and an answer, and then enabling the vectors of all candidate answers to be used for selectinganswers; S2, carrying out experiment. According to the model provided by the invention, paragraphs are firstly segmented into blocks under multiple granularities; an encoder is used for summing the intra-block word embedding vectors by utilizing neural word bag expression; then, a relationship between blocks with different granularities where each word is located through a two-layer forward neuralnetwork is modeled to construct a gating function, so that the model has greater context information and captures paragraph internal reasoning at the same time. Compared with a baseline neural network model such as a Stanford AR model and a GA Reader, the accuracy of the model is improved by 9-10%. Compared with a recent model SurfaceLR, the accurcay is at least improved by 3% and is about 1% higher than that of a single model of the TriAN, and in addition, the model effect can also be improved through pre-training on an RACE data set.

Embodiments of the invention can include methods and systems for controlling clearances in a turbine. In one embodiment, a method can include applying at least one operating parameter as an input to at least one neural network model, modeling via the neural network model a thermal expansion of at least one turbine component, and taking a control action based at least in part on the modeled thermal expansion of the one or more turbine components. An example system can include a controller operable to determine and apply the operating parameters as inputs to the neural network model, model thermal expansion via the neural network model, and generate a control action based at least in part on the modeled thermal expansion.

The invention discloses an abnormal gait identification method capable of facilitating screening Parkinsonism. The method is characterized in that the gait plantar pressure characteristics can be extracted, and the modeling and the identification of the neural network of the gait system of the normal healthy people and the patients suffering from the Parkinsonism can be dynamically carried out; the constant neutral network can be established; a dynamic estimator can be built by using the constant neutral network, and based on the difference between the gait modes of the normal healthy people and the patients suffering from the Parkinsonism in the gait system dynamics, the abnormal gait caused by the Parkinsonism and the normal gait of the normal healthy people can be distinguished according to the minimum error principle, and the screening detection of the Parkinsonism can be facilitated. By arranging a pressure sensing floor system or wearing the special shoes provided with the pressure sensor insole, the plantar pressure characteristics can be acquired, and the abnormal gait caused by the Parkinsonism and the normal gait of the normal healthy people can be distinguished conveniently, simply, and non-invasively, and therefore the daily gait monitoring of the family members can be realized, and the screening detection of the Parkinsonism can be facilitated.

The invention discloses an abnormal gait detection method based on a determined learning theory. The abnormal gait detection method based on the determined learning theory includes the steps that features are extracted, neural network modeling and identification are dynamically carried out on a gait system of healthy and normal people and patients with motor neurodegenerative diseases of different types on the basis of the extracted gait features, a constant neural network is built, a dynamic estimator is built by the utilization of the constant neural network, and abnormal gaits caused by the motor neurodegenerative diseases are distinguished from normal gaits of the general healthy people according to the minimum error principle on the basis of differences between the gait mode of the healthy and normal people and the gait mode of the patients with the motor neurodegenerative diseases of different types on the gait system dynamics, so that the abnormal gaits are detected accurately and a detection result is evaluated. The abnormal gait detection method based on the determined learning theory has the advantages that the method is convenient and easy to implement and is in a non-invasion mode, and under the intelligent home environment, daily gait monitoring on family members can be achieved by mounting a pressure sensing floor system or wearing special shoes with sensor insoles.

The present invention relates to a rockburst dynamic prediction method based on BP neural network modeling. The method comprises the steps of: determining and acquiring rockburst influence factors; performing quantification processing on qualitative description parts in influence factor indexes, and obtaining an initial population; performing BP neural network training on the eight acquired influence factors separately; optimizing a number of neurons, an algorithm learning rate and momentum factors by using a genetic algorithm, and obtaining an optimal hidden layer node number; and performing prediction on rockburst of a mine by using a BP neural network algorithm model obtained through training, and obtaining a risk level of the rockburst of the mine. The method provided by the present invention has relatively high reliability, overcomes the defect of no association between the rockburst and the influence factors of the rockburst in the current rockburst prediction process, implements middle and short term dynamic prediction on the rockburst, and can be widely applied to the field of mine rockburst prediction.

The invention discloses a real-time yield predicting method for a catalytic cracking device. According to the real-time yield predicting method for the catalytic cracking device, kinetic parameters and device parameters of a catalytic cracking reaction are corrected in real time by processing field real-time data by adopting a data reconciliation technology, and combining an improved differential evolution algorithm, so that the actual operating situations of the device can be described accurately by using a catalytic cracking device mechanism model. The method comprises the following steps: on the basis of a corrected model, analyzing the influence on the yield of a catalytic cracking product caused by key operation / process conditions, such as an operating temperature, a feeding load, a raw material preheating temperature, a reaction pressure, a residue adding ratio, a regenerator temperature, a catalyst-to-oil ratio and the like; performing piecewise linearization according to an influence trend, solving a linear equation to obtain corresponding Delta-Base yield data, associating the operating conditions and the Delta-Base yield data by combining a neural network modeling technology, and establishing a yield agent model, so that the yield data calculating speed is improved; the real-time yield predicting of a continuous catalytic cracking device is realized; a theoretical support is provided for establishing an accurate plan optimization PIMS model.

The invention discloses a micro-gyroscope control system based on a neural network. The micro-gyroscope control system comprises a control system which is in connection communication with a micro-gyroscope, wherein the control system comprises a nominal controller and a neural network compensator. The control scheme of a feedback controller based on a micro-gyroscope nominal model and the neural network compensator is applicable to control of the micro-gyroscope, so that the tracking effect and robustness of the micro-gyroscope control system are improved. The adaptive law of a weight value of the neural network is designed based on a Lyapunov stability theory, so that the stability of the whole control system can be guaranteed under the condition of presence of a modeling error of the neural network.

The invention belongs to the field of equipment maintenance time prediction, and relates to a selective wear-based equipment optimal maintenance time prediction method. The method mainly comprises two steps of: solving a selective wear possibility value of each part of equipment in the current state by utilizing an association rule algorithm; and taking the solved possibility value as input, and solving the optimal maintenance time by neural network modeling. The method comprises the following steps of: constructing an association rule library; acquiring state monitoring data, extracting characteristic values from the data, and establishing an equipment monitoring data set; matching the equipment monitoring data set with the association rule library, and calculating the wear possibility value of each part under the condition of successful matching; and training a self-organizing competitive neural network model, and predicting the optical maintenance time by utilizing the model.

Based on principle of minimization risk of configuration, combined with cooperative collaboration evolution algorithm, and learning network structure of neural network and connection weight value, the invention obtains optimal compromise between network structure and connection weight value finally. The method includes three basic steps: data processing, network learning and network estimated forecast. Configuring network and learning connection weight value are carried out at same time in the invention so as to better solve practical problems existed in traditional neural network learning: correlation between result and initial value, slow convergence rate, easy to run to local minimum value as well as derivable error function needed and over learning. The invention raises learning capability and generalization capability of network, applicable to intelligent diagnosing heart disease, fault diagnosis in industries, stock and goods price forecasting etc.

A method for a computer-aided control of a technical system is provided. The method involves use of a cooperative learning method and artificial neural networks. In this context, feed-forward networks are linked to one another such that the architecture as a whole meets an optimality criterion. The network approximates the rewards observed to the expected rewards as an appraiser. In this way, exclusively observations which have actually been made are used in optimum fashion to determine a quality function. In the network, the optimum action in respect of the quality function is modeled by a neural network, the neural network supplying the optimum action selection rule for the given control problem. The method is specifically used to control a gas turbine.