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Temporal Self-organization for Neural Networks

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Temporal Self-organization for Neural Networks

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Recurrent Neural Networks for Temporal Data Processing

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Introduction to Neural Networks for Java, Second Edition

"Introduction to Neural Networks for Java" introduces the Java programmer to the world of Neural Networks and Artificial Intelligence;

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Information-Theoretic Aspects of Neural Networks

considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only;

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Advances in Neural Computation, Machine Learning, and Cognitive Research IV

recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXII International Conference on;

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Spiking Neural Networks

temporal data instead of values within a time window (rate code). The input data into such a neural network arrives in the shape of sequence;

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Advances in Neural Computation, Machine Learning, and Cognitive Research III

scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self;

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Advances in Neural Computation, Machine Learning, and Cognitive Research III

scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self;

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Sensory Neural Networks

behavior, their relation to recent activity in neural networks and connectionist systems, their use for image processing applications, and their;

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Revival: Sensory Neural Networks (1991)

behavior, their relation to recent activity in neural networks and connectionist systems, their use for image processing applications, and their;

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Evolutionary Machine Learning Techniques

neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy;

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Evolutionary Machine Learning Techniques

neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy;

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Principles Of Artificial Neural Networks

course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers;

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Handbook on Neural Information Processing

Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple;

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Cellular Neural Networks, Multi-scroll Chaos And Synchronization

. Cellular neural networks and their universal machine implementations offer a well-established platform for processing spatial-temporal patterns and;

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Modeling Brain Function

they have stimulated. Substantial progress in understanding memory, the learning process, and self-organization by studying the properties;

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Fuzzy Neural Intelligent Systems

of the theories, algorithms, and designs of systems for specific applications. There has been little theoretical support for fuzzy neural systems;

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Neural Networks for Applied Sciences and Engineering

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering;

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SelfOrganization in Sensor and Actor Networks

discovered that imitate the properties of self-organization. Some algorithms for on-demand communication and coordination, including data-centric;

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The Relevance of the Time Domain to Neural Network Models

time domain can be effectively employed in neural network models. A first direction to consider is to deploy oscillators that model temporal;

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Supervised Sequence Labelling with Recurrent Neural Networks

, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and;

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Kinematic Control of Redundant Robot Arms Using Neural Networks

. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural;

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Principles Of Artificial Neural Networks (3rd Edition)

Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different;

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Neural Networks

at ways of implementing a neural network, including electronic and optical systems This book is suitable for undergraduates from Computer;

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Pattern Recognition Using Neural Networks

text is suitable for an advanced undergraduate course in pattern recognition or neural networks, and is also useful as a reference and a;

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Quantum Computing

network systems. Specifically, a smart network field theory (conventional or quantum) is a field theory for the organization of particle-many;

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