Recent Advances in Harmony Search Algorithm, Zong Woo Geem
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: This book presents a study of the use of optimization algorithms in complex image processing problems. The problems selected explore areas ranging from the theory of image segmentation to the detection of complex objects in medical images. Furthermore, the concepts of machine learning and optimization are analyzed to provide an overview of the application of these tools in image processing. The material has been compiled from a teaching perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, and can be used for courses on Artificial Intelligence, Advanced Image Processing, Computational Intelligence, etc. Likewise, the material can be useful for research from the evolutionary computation, artificial intelligence and image processing communities.
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.
Описание: Throughout time, scientists have looked to nature in order to understand and model solutions for complex real-world problems. In particular, the study of self-organizing entities, such as social insect populations, presents a new opportunity within the field of artificial intelligence.>Emerging Research on Swarm Intelligence and Algorithm Optimization discusses current research analyzing how the collective behavior of decentralized systems in the natural world can be applied to intelligent system design. Discussing the application of swarm principles, optimization techniques, and key algorithms being used in the field, this publication serves as an essential reference for academicians, upper-level students, IT developers, and IT theorists.
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.
Автор: Zong Woo Geem Название: Harmony Search Algorithms for Structural Design Optimization ISBN: 3642034497 ISBN-13(EAN): 9783642034497 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Researchers have lately applied the metaheuristic algorithm Harmony search to handle discrete design variables for various structures such as buildings and bridges. This book gathers all the latest developments of Harmony search algorithm in structural design.
Автор: Ying-ping Chen Название: Exploitation of Linkage Learning in Evolutionary Algorithms ISBN: 3642263275 ISBN-13(EAN): 9783642263279 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This monograph examines recent progress in linkage learning, with a series of focused technical chapters that cover developments and trends in the field.
Автор: Leonid Perlovsky; Ross Deming; Roman Ilin Название: Emotional Cognitive Neural Algorithms with Engineering Applications ISBN: 3642269389 ISBN-13(EAN): 9783642269387 Издательство: Springer Рейтинг: Цена: 18284.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Dynamic logic (DL) recently had a highest impact on the development in several areas of modeling and algorithm design. Emerging areas include cognitive, emotional, intelligent systems, data mining, modeling of the mind, higher cognitive functions, evolution of languages and other.
Автор: Ying-ping Chen Название: Exploitation of Linkage Learning in Evolutionary Algorithms ISBN: 3642128335 ISBN-13(EAN): 9783642128332 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This monograph examines recent progress in linkage learning, with a series of focused technical chapters that cover developments and trends in the field.
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