Evaluation of Chess Position by Modular Neural Network Generated by Genetic Algorithm

2004 ◽  
pp. 1-10 ◽  
Author(s):  
Mathieu Autonès ◽  
Ariel Beck ◽  
Philippe Camacho ◽  
Nicolas Lassabe ◽  
Hervé Luga ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Modular Neural Network

scholarly journals Multiple-deme parallel genetic algorithm based on modular neural network for effective load shedding

2021 ◽  
Author(s):  
Ali Gholami-Rahimabadi ◽  
Hadi Razmi ◽  
Hasan Doagou-Mojarrad
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Voltage Stability ◽  
Stability Margin ◽  
Test System ◽  
Load Shedding ◽  
Parallel Genetic Algorithm ◽  
Detailed Model ◽  
Modular Neural Network ◽  
Voltage Stability Margin

Abstract One of the most effective corrective control strategies to prevent voltage collapse and instability is load shedding. In this paper, a multiple-deme parallel genetic algorithm (MDPGA) is used for a suitable design of load shedding. The load shedding algorithm is implemented when the voltage stability margin index of the power system is lower than a predefined value. In order to increase the computational speed, the voltage stability margin index is estimated by a modular neural network method in a fraction of a second. In addition, in order to use the exact values of the voltage stability margin index for neural network training, a simultaneous equilibrium tracing technique has been employed considering the detailed model of the components of the generating units such as the governor and the excitation system. In the proposed algorithm, the entire population is partitioned into several isolated subpopulations (demes) in which demes distributed in different processors and individuals may migrate occasionally from one subpopulation to another. The proposed technique has been tested on New England-39 bus test system and the obtained results indicate the efficiency of the proposed method.

scholarly journals Customer Churn Prediction in Telecommunication Industry Using Data Mining Methods

2020 ◽  
Vol 8 (1) ◽  
pp. 1-8
Author(s):  
Homa Meghyasi ◽  
Abas Rad
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Churn Prediction ◽  
Modular Neural Network ◽  
Customer Churn ◽  
Important Challenge ◽  
Accuracy Result ◽  
Customer Churn Prediction ◽  
Using Data

At present, in competitive space between companies and organizations, customers churn is their most important challenge. When a customer becomes churn, organizations lose one of their most important assets, which can lead to financial losses and even bankruptcy.  Customer churn prediction using data mining techniques can alleviate these problems to some extent.  The aim of the present study is to provide a hybrid method based on Genetic Algorithm and Modular Neural Network to customer churn prediction in telecommunication industries and use Irancell data as a sample. The accuracy result of this study which is 95.5% get the highest accuracy rank in comparisons with the result of other methods, which shows using modular neural network with two modules of feedforward neural network and also using genetic algorithm to obtain optimal structure for modules of the neural network are the most important indicators of this method to each the highest accuracy result among the rest of methods.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

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