Título: Complex-Valued Neural Networks with Multi-Valued Neurons: 353
Autor: Igor Aizenberg
Sinopse: Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts.This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information.These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories. The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence.
Contexto da obra
Quando a classificação é mais ampla, o contexto do livro costuma depender ainda mais de autoria, tema e edição. “Complex-Valued Neural Networks with Multi-Valued Neurons: 353”, de Igor Aizenberg, publicado pela editora Springer, em 2011 e com 280 páginas, integra a categoria Livros Variados. Por isso, autoria, edição e tema acabam tendo ainda mais peso na forma de apresentar o livro.
Editora: Springer
Páginas: 280
Ano: 2011
Edição: 2011
Linguagem: pt_BR
ISBN: 9783642203527
ISBN13: 9783642203527
Sobre a editora
Os livros da editora Springer apresentam uma leitura densa e focada em temas acadêmicos e científicos, com ênfase em áreas como matemática avançada, ciências naturais, tecnologia e ciências da saúde. A experiência de leitura costuma exigir familiaridade com linguagem técnica e conceitos especializados, refletindo o rigor das pesquisas e análises aprofundadas. O tom varia entre o didático e o expositivo, com obras que vão desde apresentações formais de teorias até relatos detalhados de estudos de caso e revisões sistemáticas. O catálogo sugere uma predominância de textos que dialogam com públicos acadêmicos e profissionais, oferecendo conteúdos que se apoiam em fundamentos históricos, dados empíricos e metodologias precisas.
