Titlebook: Dealing with Complexity; A Neural Networks Ap Mirek Kárný,Kevin Warwick,Vera Kůrková Book 1998 Springer-Verlag London Limited 1998 artifici

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Incremental Approximation by Neural Networks,xed architecture which requires us to solve a non-linear optimization problem in a multidimensional parameter space. An alternative approach is to use a . and determine the final set of network parameters in a series of steps, each taking place in a lower dimensional space. There have been considere

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Rates of Approximation in a Feedforward Network Depend on the Type of Computational Unit, ([19], [8], [1], [2], [13]). Mhaskar and Micchelli have shown in [22] that a network using any non-polynomial locally Riemann integrable activation can approximate any continuous function of any number of variables on a compact set to any desired degree of accuracy (i.e. it has the universal approx

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Recent Results and Mathematical Methods for Functional Approximation by Neural Networks,empts to find a parameter vector . such that .|| < ., where . denotes the input-output function produced by a neural network architecture . using as “weights” .. When the input dimension is . and the output dimension is 1, .: R. → .. For example, if . is the standard perceptron architecture with act

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Differential Neurocontrol of Multidimensional Systems,problems. Learning ability is one of their main advantages, and special learning algorithms provide rather good convergence. They do not require precise initial mathematical models that can be developed during the adaptation process. Generalization properties may ensure solving such situations in th

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The Psychological Limits of Neural Computation,mputable function is Turing computable. The languages accepted by Turing machines form the recursively enumerable language family .. and, according to the Church-Turing thesis, .. is also the class of algorithmic computable sets. In spite of its generality, the Turing model . solve any problem. Reca

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Lecture Notes in Computer ScienceRecurrent nets have been introduced in control, computation, signal processing, optimization, and associate memory applications. Given matrices . ∈ ℝ.., . ∈ ℝ.., . ∈ ℝ.., as well as a fixed Lipschitz scalar function . : ℝ → ℝ, the . Σ with . and . (., .,.) is given by: .where.: ℝ. → ℝ. is the diagonal map

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Recurrent Neural Networks: Some Systems-Theoretic Aspects,Recurrent nets have been introduced in control, computation, signal processing, optimization, and associate memory applications. Given matrices . ∈ ℝ.., . ∈ ℝ.., . ∈ ℝ.., as well as a fixed Lipschitz scalar function . : ℝ → ℝ, the . Σ with . and . (., .,.) is given by: .where.: ℝ. → ℝ. is the diagonal map

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A Brain-Like Design to Learn Optimal Decision Strategies in Complex Environments,In the development of learning systems and neural networks, the issue of complexity occurs at many levels of analysis.