A new improved CMAC neural network

2010 ◽  
Author(s):  
Yingqi Ge ◽  
Xiaoping Luo ◽  
Pengying Du
Keyword(s):  
Neural Network ◽  
Cmac Neural Network

scholarly journals Applying a Cerebellar Model Articulation Controller Neural Network to a Photovoltaic Power Generation System Fault Diagnosis

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Bo-Jyun Liao ◽  
Chin-Pao Hung
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Fault Diagnosis ◽  
Cerebellar Model Articulation Controller ◽  
Training Process ◽  
Cmac Neural Network ◽  
Photovoltaic Power ◽  
Training Samples ◽  
Parallel Connections ◽  
Model Features

This study employed a cerebellar model articulation controller (CMAC) neural network to conduct fault diagnoses on photovoltaic power generation systems. We composed a module array using 9 series and 2 parallel connections of SHARP NT-R5E3E 175 W photovoltaic modules. In addition, we used data that were outputted under various fault conditions as the training samples for the CMAC and used this model to conduct the module array fault diagnosis after completing the training. The results of the training process and simulations indicate that the method proposed in this study requires fewer number of training times compared to other methods. In addition to significantly increasing the accuracy rate of the fault diagnosis, this model features a short training duration because the training process only tunes the weights of the exited memory addresses. Therefore, the fault diagnosis is rapid, and the detection tolerance of the diagnosis system is enhanced.

Adaptive Learning Algorithm for Cerebellar Model Articulation Controller: Neural Network Based Hybrid-Type Controller—Part I

2000 ◽  
Author(s):  
Magdy Mohamed Abdelhameed ◽  
Sabri Cetinkunt
Keyword(s):  
Neural Network ◽  
Control System ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Cerebellar Model Articulation Controller ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Type ◽  
Cmac Neural Network ◽  
Learning Technique

Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learning algorithm, especially when it is used in a hybrid- type controller. This work is intended to introduce a simulation study for examining the performance of a hybrid-type control system based on the conventional learning algorithm of CMAC neural network. This study showed that the control system is unstable. Then a new adaptive learning algorithm of a CMAC based hybrid- type controller is proposed. The main features of the proposed learning algorithm, as well as the effects of the newly introduced parameters of this algorithm have been studied extensively via simulation case studies. The simulation results showed that the proposed learning algorithm is a robust in stabilizing the control system. Also, this proposed learning algorithm preserved all the known advantages of the CMAC neural network. Part II of this work is dedicated to validate the effectiveness of the proposed CMAC learning algorithm experimentally.

CMAC Based Hybrid Control System for Solving Electrohydraulic System Nonlinearities

2014 ◽  
Vol 4 (2) ◽  
pp. 47-72
Author(s):  
Amro Shafik ◽  
Magdy Abdelhameed ◽  
Ahmed Kassem
Keyword(s):  
Neural Network ◽  
Control System ◽  
Hybrid Control ◽  
Learning Algorithm ◽  
Cerebellar Model Articulation Controller ◽  
Stick Slip ◽  
Electrohydraulic System ◽  
Cmac Neural Network ◽  
Wide Range ◽  
Hybrid Control System

Automation based electrohydraulic servo systems have a wide range of applications in nowadays industry. However, they still suffer from several nonlinearities like deadband in electrohydraulic valves, hysteresis, stick-slip friction in valves and cylinders. In addition, all hydraulic system parameters have uncertainties in their values due to the change of temperature while working. This paper addresses these problems by designing a suitable intelligent control system that has the ability to deal with the system nonlinearities and parameters uncertainties using a fast and online learning algorithm. A novel hybrid control system based on Cerebellar Model Articulation Controller (CMAC) neural network is presented. The proposed controller is composed of two parallel controllers. The first is a conventional Proportional-Velocity (PV) servo type controller which is used to decrease the large initial error of the closed-loop system. The second is a CMAC neural network which is used as an intelligent controller to overcome nonlinear characteristics of the electrohydraulic system. A fourth order model for the electrohydraulic system is introduced. PV controller parameters are tuned to get optimal values. Simulation and experimental results show a good tracking performance obtained using the proposed controller. The controller shows its robustness in two working environments. The first is by adding different inertia loads and the second is working with noisy level input signals.

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