Neural Network Fuzzy Logic And Genetic Algorithm Pdf

neural network fuzzy logic and genetic algorithm pdf

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The book is written by S. Rajasekaran and Dr G. Vijayalakshmi Pai.

Neural networks, fuzzy logic, and genetic algorithms : synthesis and applications

Due to the continuous increase of the population and the perpetual progress of industry, the energy management presents nowadays a relevant topic that concerns researchers in electrical engineering. Indeed, in order to establish a good exploitation of the electrical grid, it is necessary to solve technical and economic problems. This can only be done through the resolution of the Unit Commitment Problem. However, each production unit has some constraints that make this problem complex, combinatorial, and nonlinear. This paper presents a comparative study between a strategy based on hybrid gradient-genetic algorithm method and two strategies based on metaheuristic methods, fuzzy logic, and genetic algorithm, in order to predict the combinations and the unit commitment scheduling of each production unit in one side and to minimize the total production cost in the other side. To test the performance of the optimization proposed strategies, strategies have been applied to the IEEE electrical network 14 busses and the obtained results are very promising. According to human activities, the electrical energy consumption is still increasing.

A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks ANN and Fuzzy Logic FL have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions MFs. Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm GA — as a well-known technique to solve the complex optimization problems — is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input.

Request PDF | The merging of neural networks, fuzzy logic, and genetic algorithms | During the last decade, there has been increased use of.


This paper presents a comprehensive review of soft computing applications in the domain of fabrics and clothing. In the last two decades, soft computing techniques, such as artificial neural network, fuzzy logic and genetic algorithm, have been used abundantly for fabrics and clothing modelling, manufacturing, quality control and marketing. This review is aimed at presenting a compendium of research work done so far on the applications of soft computing techniques in fabrics and clothing science and engineering. In the beginning of the paper, a brief introduction of soft computing techniques is provided. Then, the applications of soft computing methods in fabric property modelling tensile, bending, shear, drape, handle, comfort, thickness and compression, air permeability, porosity, etc.

Stroke is a global pandemic, affecting both developed and developing countries. In Nigeria, a steady rise in affected patients is becoming noticeable to all which inspired the development of this research. Stroke is caused by high blood pressure, smoking cigarettes, family history of stroke, high cholesterol, diabetes, obesity, overweight and cardiovascular diseases which affect the brain and damage part of the body legs, hand coordinated by that part of the brain.

Soft Computing: Fuzzy Logic, Neural Networks, and Genetic Algorithms

PHI Learning Pvt. This book provides comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence.

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Save extra with 2 Offers. Rajasekaran, G. Vijayalakshmi Pai Book Summary: The second edition of this book provides a comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence, which in recent years, has turned synonymous to it. The constituent technologies discussed comprise neural network NN , fuzzy system FS , evolutionary algorithm EA , and a number of hybrid systems, which include classes such as neuro-fuzzy, evolutionary-fuzzy, and neuro-evolutionary systems. Every architecture has been discussed in detail through illustrative examples and applications.

Exploring Computer Science with Scheme pp Cite as. Soft computing is a relatively new field within computer science. It is a conglomeration of fuzzy logic, neural networks, and probabilistic reasoning.


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