Описание: Introduction.- Theory and Background.- Problems Statement.- Methodology.- Simulation Results.- Statistical Analysis and Comparison of Results.
Описание: This book focuses on the fields of fuzzy logic, bio-inspired algorithm, especially the differential evolution algorithm and also considering the fuzzy control area.
Автор: Wesam Ashour Barbakh; Ying Wu; Colin Fyfe Название: Non-Standard Parameter Adaptation for Exploratory Data Analysis ISBN: 3642040047 ISBN-13(EAN): 9783642040047 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A review of standard algorithms provides the basis for more complex data mining techniques in this overview of exploratory data analysis. Recent reinforcement learning research is presented, as well as novel methods of parameter adaptation in machine learning.
Описание: In this book a new model for data classification was developed. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types of soil.
Автор: Zong Woo Geem Название: Recent Advances in Harmony Search Algorithm ISBN: 3642263178 ISBN-13(EAN): 9783642263170 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces major advances of the harmony search algorithm in recent years. Topics cover theory and application and include robotics, visual tracking, web text data mining, power flow planning, fuzzy control system, irrigation, logistics and more.
Автор: Zong Woo Geem Название: Recent Advances in Harmony Search Algorithm ISBN: 364204316X ISBN-13(EAN): 9783642043161 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces major advances of the harmony search algorithm in recent years. Topics cover theory and application and include robotics, visual tracking, web text data mining, power flow planning, fuzzy control system, irrigation, logistics and more.
Автор: Zong Woo Geem Название: Music-Inspired Harmony Search Algorithm ISBN: 3642101240 ISBN-13(EAN): 9783642101243 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: There exists an analogy between music and optimization. This book focuses on a music-inspired metaheuristic algorithm, harmony search. It details both theoretical backgrounds and practical applications of harmony search algorithms.
Описание: For this reason we consider in this book the proposed method using fuzzy systems, fuzzy controllers, and bee colony optimization algorithm improve the behavior of the complex control problems.
Автор: Joong Hoon Kim; Zong Woo Geem Название: Harmony Search Algorithm ISBN: 3662479257 ISBN-13(EAN): 9783662479254 Издательство: Springer Рейтинг: Цена: 34799.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Harmony Search Algorithm (HSA) is one of the most well-known techniques in the field of soft computing, an important paradigm in the science and engineering community.
In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights.
The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method.
The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for ?=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.
The optimization algorithms that were used are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods was to find the optimal type-2 fuzzy inference systems for the neural network with type-2 fuzzy weights that permit to obtain the lowest prediction error.
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