Neuro-Fuzzy Architectures and Hybrid Learning, Danuta Rutkowska
Автор: Danuta Rutkowska Название: Neuro-Fuzzy Architectures and Hybrid Learning ISBN: 3790814385 ISBN-13(EAN): 9783790814385 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Neuro-fuzzy architectures and hybrid learning are considered as intelligent systems within the framework of computational and artifical intelligence. This book provides an overview of fuzzy sets and systems, neural networks, learning algorithms, and systems which incorporates computing with words.
Автор: Skorohod Boris. A Название: Diffuse Algorithms for Neural and Neuro-Fuzzy Networks ISBN: 0128126094 ISBN-13(EAN): 9780128126097 Издательство: Elsevier Science Рейтинг: Цена: 15159.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Diffuse Algorithms for Neural and Neuro-Fuzzy Networks: With Applications in Control Engineering and Signal Processing presents new approaches to training neural and neuro-fuzzy networks. This book is divided into six chapters. Chapter 1 consists of plants models reviews, problems statements, and known results that are relevant to the subject matter of this book. Chapter 2 considers the RLS behavior on a finite interval. The theoretical results are illustrated by examples of solving problems of identification, control, and signal processing.
Properties of the bias, the matrix of second-order moments and the normalized average squared error of the RLS algorithm on a finite time interval are studied in Chapter 3. Chapter 4 deals with the problem of multilayer neural and neuro-fuzzy networks training with simultaneous estimation of the hidden and output layers parameters. The theoretical results are illustrated with the examples of pattern recognition, identification of nonlinear static, and dynamic plants.
Chapter 5 considers the estimation problem of the state and the parameters of the discrete dynamic plants in the absence of a priori statistical information about initial conditions or its incompletion. The Kalman filter and the extended Kalman filter diffuse analogues are obtained. Finally, Chapter 6 provides examples of the use of diffuse algorithms for solving problems in various engineering applications. This book is ideal for researchers and graduate students in control, signal processing, and machine learning.
Описание: Tweet Sentiment Classification Using an Ensemble of Machine Learning Supervised Classifiers Employing Statistical Feature Selection Methods.- Multiple Fuzzy Correlated Pattern Tree Mining using Minimum Item All-Confidence Thresholds.- Autonomous Visual Tracking with Extended Kalman Filter Estimator for Micro Aerial Vehicles.- Identification of Uncertain Mass and Stiffness Matrices of Multi-Storey Shear Buildings using Fuzzy Neural Network Modelling.- Fuzzy Approach to Rank Global Climate Models.- Comparative analysis of ANFIS and SVR Model performance for Rainfall Prediction.- Cross Domain Sentiment Analysis using different Machine Learning Techniques.- Combining ELM with Random Projections for Low and High Dimensional Data Classification and Clustering.- Framework for knowledge driven optimisation based data encoding for brain data modelling using spiking neural network architecture.- Short Circuit Evaluations in Gцdel Type Logic.- Coefficient of Variation based Decision Tree for Fuzzy Classification.- Predicting protein coding regions by six-base nucleotide distribution.- Ontology for Education System and Ontology based Clustering.- Neural Networks for Fast Estimation of Social Network Centrality Measures.- Scale-free memory to swiftly generate fuzzy future predictions.- Assessment of Vaccination Strategies using Fuzzy Multi-Criteria Decision Making.- Ranking and Dimensionality Reduction using Biclustering.- Effect of Fractional Order in Pole Motion.- Sewage Water Quality Index of Sewage Treatment Plant using Fuzzy MCDM Approach.- Fuzzy Formal Concept Analysis approach for Information Retrieval.- Privacy Preserving Collaborative Clustering using SOM for Horizontal Data Distribution.- A Hybrid Approach to Classification of Categorical Data based on Information-Theoretic Context Selection.- Robust Stabilization of Uncertain T-S fuzzy systems: An Integral Error Dependant Sliding Surface Design Approach.- Extreme learning machine for eukaryotic and prokaryotic promoter prediction.- A hybrid approach to the maximum clique problem in the domain of information management.- An Integrated Network Behavior and Policy based data exfiltration detection framework.- Web Usages Mining in automatic detection of Learning Style in Personalized e-Learning System.
Автор: Mario Koeppen; Nikola K. Kasabov; George Coghill Название: Advances in Neuro-Information Processing ISBN: 3642030394 ISBN-13(EAN): 9783642030390 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The two volume set LNCS 5506 and LNCS 5507 constitutes the thoroughly refereed post-conference proceedings of the 15th International Conference on Neural Information Processing, ICONIP 2008, held in Auckland, New Zealand, in November 2008.
Описание: This book is a collection of articles by leading researchers working at the cutting edge of neuro-computational modelling of neurological and psychiatric disorders. Each article contains model validation techniques used in the context of the specific problem being studied. Validation is essential for neuro-inspired computational models to become useful tools in the understanding and treatment of disease conditions. Currently, the immense diversity in neuro-computational modelling approaches for investigating brain diseases has created the need for a structured and coordinated approach to benchmark and standardise validation methods and techniques in this field of research. This book serves as a step towards a systematic approach to validation of neuro-computational models used for studying brain diseases and should be useful for all neuro-computational modellers.
Описание: This book serves as a step towards a systematic approach to validation of neuro-computational models used for studying brain diseases and should be useful for all neuro-computational modellers.
Автор: Wulfram Gerstner Название: Spiking Neuron Models ISBN: 0521890799 ISBN-13(EAN): 9780521890793 Издательство: Cambridge Academ Рейтинг: Цена: 10454.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Aimed at courses in computational neuroscience, theoretical biology, biophysics, or neural networks, this 2002 text will suit students of physics, mathematics, or computer science, as well as biologists who are interested in mathematical modelling. A large number of worked examples are embedded in the profusely-illustrated text.
Автор: Gerstner Название: Neuronal Dynamics ISBN: 1107635195 ISBN-13(EAN): 9781107635197 Издательство: Cambridge Academ Рейтинг: Цена: 8554.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Exploring neuron models, the neural code, decision making and learning, this textbook provides a thorough and up-to-date introduction to computational neuroscience for advanced undergraduate and beginning graduate students. With step-by-step explanations, end-of-chapter summaries and classroom-tested exercises, it is ideal for courses or for self-study.
Автор: Gerstner Название: Neuronal Dynamics ISBN: 1107060834 ISBN-13(EAN): 9781107060838 Издательство: Cambridge Academ Рейтинг: Цена: 15682.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Exploring neuron models, the neural code, decision making and learning, this textbook provides a thorough and up-to-date introduction to computational neuroscience for advanced undergraduate and beginning graduate students. With step-by-step explanations, end-of-chapter summaries and classroom-tested exercises, it is ideal for courses or for self-study.
This thesis addresses one of the most fundamental challenges for modern science: how can the brain as a network of neurons process information, how can it create and store internal models of our world, and how can it infer conclusions from ambiguous data? The author addresses these questions with the rigorous language of mathematics and theoretical physics, an approach that requires a high degree of abstraction to transfer results of wet lab biology to formal models.
The thesis starts with an in-depth description of the state-of-the-art in theoretical neuroscience, which it subsequently uses as a basis to develop several new and original ideas. Throughout the text, the author connects the form and function of neuronal networks. This is done in order to achieve functional performance of biological brains by transferring their form to synthetic electronics substrates, an approach referred to as neuromorphic computing. The obvious aspect that this transfer can never be perfect but necessarily leads to performance differences is substantiated and explored in detail.
The author also introduces a novel interpretation of the firing activity of neurons. He proposes a probabilistic interpretation of this activity and shows by means of formal derivations that stochastic neurons can sample from internally stored probability distributions. This is corroborated by the author’s recent findings, which confirm that biological features like the high conductance state of networks enable this mechanism. The author goes on to show that neural sampling can be implemented on synthetic neuromorphic circuits, paving the way for future applications in machine learning and cognitive computing, for example as energy-efficient implementations of deep learning networks.
The thesis offers an essential resource for newcomers to the field and an inspiration for scientists working in theoretical neuroscience and the future of computing.
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