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New Classification Method Based on Modular Neural Networks with the LVQ Algorith

In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector;

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Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation

In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach;

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New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension

as a hybrid intelligent system combining modular neural net-works and fuzzy systems. The different responses of the hybrid system are;

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Learning and Categorization in Modular Neural Networks

the arousal system of these models. A concise introduction to genetic algorithms, a new computing method based on the biological metaphor;

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Evolutionary Machine Learning Techniques

networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews;

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Evolutionary Machine Learning Techniques

networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions;

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Pattern Recognition with Neural Networks in C++

toward the practitioner, Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the;

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Pattern Recognition with Neural Networks in C++

toward the practitioner, Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the;

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Evolutionary Algorithms and Neural Networks

This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At;

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Credit Rating Modelling by Neural Networks

This book presents the modelling possibilities of neural networks on a complex real-world problem, i.e. credit rating process modelling;

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Recent Advances in Artificial Neural Networks

collects the latest neural network paradigms and reports on their promising new applications. World-renowned experts discuss the use of neural;

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Supervised and Unsupervised Pattern Recognition

research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy;

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Supervised Learning with Complex-valued Neural Networks

monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The;

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Analysis and Classification of EEG Signals for Brain-Computer Interfaces

of changes in the EEG signal based on facial expressions. Further topics touch on machine learning, deep learning, and neural networks. The book also;

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Analysis and Classification of EEG Signals for Brain Computer Interfaces

of changes in the EEG signal based on facial expressions. Further topics touch on machine learning, deep learning, and neural networks. The book also;

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Kinematic Control of Redundant Robot Arms Using Neural Networks

neural network based learning and control co-design for Stewart platform control. The book finishes with a section on zeroing neural networks for;

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Neural Network Learning

learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks;

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Neural Network Learning

learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks;

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Semi-empirical Neural Network Modeling and Digital Twins Development

-layered neural networks built on the finite element method (FEM). The strength of the new method presented in this book is the automatic inclusion;

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Advanced Deep Learning with R

branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will;

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New Developments in Computer Networks

of prediction performance based on real network traffic; network security; a modified artificial neural network based approach to add new data in a;

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Pattern Recognition and Image Preprocessing

new and expanded sections on neural networks, Fisher's discriminant, wavelet transform, and the method of principal components. It contains;

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Pattern Classification

Classification sheds new light on the relationship between seemingly unrelated approaches to pattern recognition, including statistical methods;

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Neural Networks for Applied Sciences and Engineering

. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic;

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New Developments In Artificial Neural Networks Research

in drilling GFRE using artificial neural networks; ANN-based approaches to study the nanoscale CMOS devices; artificial neural networks;

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New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks

In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed;

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Complex-Valued Neural Networks with Multi-Valued Neurons

become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued;

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