Research of FWP Process Deformation Compensation Forecasting on the Basis of TS-FNN

2011 ◽  
Vol 295-297 ◽  
pp. 2430-2437 ◽  
Author(s):  
Yao Hua Deng ◽  
Gui Xiong Liu ◽  
Qing Fu Liao ◽  
Lei Zeng ◽  
Li Ming Wu
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Descent Method ◽  
Steepest Descent Method ◽  
National Standard ◽  
Function Parameter ◽  
Parameter Study ◽  
Network Parameter ◽  
Fuzzy Neural ◽  
Order Of Magnitude

After analyzing the influencing factors of flexible workpiece path(FWP) process deformation, this article proposes the basic conception of process deformation intelligent forecasting and compensation, start from the process modeling method of Takagi-Sugeno fuzzy neural network, to modify the classic FNN model and construct the multiple input/output TS-FNN model for FWP process control; with LMS law and steepest descent method, antecedent network membership function parameter adjustment and descent network parameter study method of TS-FNN model is deduced; finally to carry on comprehensive simulation on TS-FNN model, the result shows the constructed model is better than BP neural network and RBF neural network for an order of magnitude on predication accuracy; in the quilting process of flexible objects, compensated by TS-FNN, the path processing obtains good approaching effect, testing result indicates that the position error scope of quilting is from 0.078 to 0.162(mm), the accuracy is higher than excellence grade of quilting which refers to national standard FZ/T81005-2006.

AN Improved RBF Neural Network Model Based on Hybrid Learning Algorithm

2013 ◽  
Vol 718-720 ◽  
pp. 2202-2207
Author(s):  
Zhao Hu Deng ◽  
Yan Qin Zhang
Keyword(s):  
Neural Network ◽  
Traditional Method ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Clustering Algorithms ◽  
Hybrid Learning ◽  
Descent Method ◽  
Learning Ability ◽  
Steepest Descent Method ◽  
Training Samples

When building the radial basis function (RBF) neural network with traditional method, the property of the network is easily influenced by the distribution of training samples. The learning ability and generalization ability are hard to achieve the optimum. In this paper, it presents a new method to solve this problem. In the method it replaced the traditional clustering algorithms with genetic algorithms to optimize the distribution of RBF. At the same time it combined the steepest descent method with GA to solve the binary defect of GA encoding. After experiments the results showed that the constructed neural network has a better architecture and more accuracy than that built with traditional method.

RELATIONSHIP BETWEEN A FUZZY LOGIC AND A STEEPEST DESCENT APPROACH TO OPTIMIZE A FEEDFORWARD ARTIFICIAL NEURAL NETWORK CONFIGURATION

1994 ◽  
Vol 05 (04) ◽  
pp. 299-312
Author(s):  
ROBERT N. SHARPE ◽  
MO-YUEN CHOW
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mathematical Models ◽  
Cost Function ◽  
Evaluation Criteria ◽  
Descent Method ◽  
Steepest Descent ◽  
Steepest Descent Method ◽  
Network Configuration ◽  
Training Time

The neural network designer must take into consideration many factors when selecting an appropriate network configuration. The performance of a given network configuration is influenced by many different factors such as: accuracy, training time, sensitivity, and the number of neurons used in the implementation. Using a cost function based on the four criteria mentioned previously, the various network paradigms can be evaluated relative to one another. If the mathematical models of the evaluation criteria as functions of the network configuration are known, then traditional techniques (such as the steepest descent method) could be used to determine the optimal network configuration. The difficulty in selecting an appropriate network configuration is due to the difficulty involved in determining the mathematical models of the evaluation criteria. This difficulty can be avoided by using fuzzy logic techniques to perform the network optimization as opposed to the traditional techniques. Fuzzy logic avoids the need of a detailed mathematical description of the relationship between the network performance and the network configuration, by using heuristic reasoning and linguistic variables. A comparison will be made between the fuzzy logic approach and the steepest descent method for the optimization of the cost function. The fuzzy optimization procedure could be applied to other systems where there is a priori information about their characteristics.

A demonstration of using the model reference principle to develop the function-oriented adaptive pulse-coded neural network

SIMULATION ◽  
2019 ◽  
Vol 96 (2) ◽  
pp. 207-219
Author(s):  
B Lungsi Sharma ◽  
Richard B Wells
Keyword(s):  
Neural Network ◽  
Descent Method ◽  
Design Tool ◽  
Steepest Descent Method ◽  
Network Systems ◽  
Model Reference ◽  
Physiological Models ◽  
Psychological Phenomena ◽  
Simple Mapping

How can one design an adaptive pulsed neural network that is based on psycho-phenomenological foundations? In other words, how can one migrate the adaptive capability of a psychologically modeled neural network to a pulsed network? Neural networks that model psychological phenomena are at a larger scale than physiological models. There is a common presumption that pulse-coded neural network analogs to non-pulsing networks can be obtained by a simple mapping and scaling process of some sort. But the actual in vivo environment of pulse-coded neural network systems produces a much more diverse set of firing patterns. Thus, functional mapping from traditional neural network systems to pulse-coded neural network systems is much more challenging than has been presumed. This paper demonstrates that the employment of model reference adaptation as a method for applying scientific reduction is a powerful design tool for the development of a function-oriented adaptive pulse-coded neural network. The performance surface is empirically obtained by comparing the performance of the pulsed network to the non-pulsing network. Based on this surface, the adaptive algorithm is a combination of gain scheduling and steepest-descent method. Therefore, the adaptive property of the pulse-coded neural network is built upon a psycho-physiological foundation.

scholarly journals Fuzzy Neural Network Q-Learning Method for Model Disturbance Change: A Deployable Antenna Panel Application

2019 ◽  
Vol 2019 ◽  
pp. 1-21
Author(s):  
Zhiyong Liu ◽  
Hong Bao ◽  
Song Xue ◽  
Jingli Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Basis Function ◽  
Fuzzy Neural Network ◽  
Rbf Neural Network ◽  
Learning Method ◽  
Active Deformation ◽  
Q Learning ◽  
Adjustment Mechanism ◽  
Fuzzy Neural

This paper addresses the disturbance change control problem with an active deformation adjustment mechanism on a 5-meter deployable antenna panel. A fuzzy neural network Q-learning control (FNNQL) strategy is proposed in this paper for the disturbance change to improve the accuracy of the antenna panel. In the proposed method, the error of the model disturbance is reduced by introducing the fuzzy radial basis function (RBF) neural network into Q-learning, and the parameters of the fuzzy RBF neural network were optimized and adjusted by a Q-learning method. This allows the FNNQL controller to have a strong adaptability to deal with the disturbance change. Finally, the proposed method has been adopted in the middle plate of a 5-meter deployable antenna panel, and it was found that the method could successfully adapt the model disturbance change in the antenna panel. Results of the simulation also show that the whole control system meets the required accuracy requirements.

scholarly journals Hessian with Mini-Batches for Electrical Demand Prediction

2020 ◽  
Vol 10 (6) ◽  
pp. 2036 ◽  
Author(s):  
Israel Elias ◽  
José de Jesús Rubio ◽  
David Ricardo Cruz ◽  
Genaro Ochoa ◽  
Juan Francisco Novoa ◽  
...  
Keyword(s):  
Neural Network ◽  
Descent Method ◽  
Steepest Descent ◽  
Steepest Descent Method ◽  
Demand Prediction ◽  
The Neural Network ◽  
Electrical Demand

The steepest descent method is frequently used for neural network tuning. Mini-batches are commonly used to get better tuning of the steepest descent in the neural network. Nevertheless, steepest descent with mini-batches could be delayed in reaching a minimum. The Hessian could be quicker than the steepest descent in reaching a minimum, and it is easier to achieve this goal by using the Hessian with mini-batches. In this article, the Hessian is combined with mini-batches for neural network tuning. The discussed algorithm is applied for electrical demand prediction.

scholarly journals Modified neural network based cascaded control for product composition of reactive distillation

2016 ◽  
Vol 18 (2) ◽  
pp. 111-121 ◽  
Author(s):  
Vandana Sakhre ◽  
Sanjeev Jain ◽  
V. S. Sapkal ◽  
D.P. Agarwal
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Control Strategy ◽  
Reactive Distillation ◽  
Descent Method ◽  
Steepest Descent ◽  
Steepest Descent Method ◽  
Mean Square ◽  
Loop Control ◽  
Single Loop

Abstract In this research work, neural network based single loop and cascaded control strategies, based on Feed Forward Neural Network trained with Back Propagation (FBPNN) algorithm is carried out to control the product composition of reactive distillation. The FBPNN is modified using the steepest descent method. This modification is suggested for optimization of error function. The weights connecting the input and hidden layer, hidden and output layer is optimized using steepest descent method which causes minimization of mean square error and hence improves the response of the system. FBPNN, as the inferential soft sensor is used for composition estimation of reactive distillation using temperature as a secondary process variable. The optimized temperature profile of the reactive distillation is selected as input to the neural network. Reboiler heat duty is selected as a manipulating variable in case of single loop control strategy while the bottom stage temperature T9 is selected as a manipulating variable for cascaded control strategy. It has been observed that modified FBPNN gives minimum mean square error. It has also been observed from the results that cascaded control structure gives improved dynamic response as compared to the single loop control strategy.

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