Approximation by superpositions of a sigmoidal function

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• Multilayer feedforward networks are universal approximators→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Approximation capabilities of multilayer feedforward networks→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Universal Approximation Using Radial-Basis-Function Networks→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Orthogonal least squares learning algorithm for radial basis function networks→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Evolving Neural Networks through Augmenting Topologies→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Generating fuzzy rules by learning from examples→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Universal approximation bounds for superpositions of a sigmoidal function→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Fuzzy basis functions, universal approximation, and orthogonal least-squares learning→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Universal Approximation using Incremental Constructive Feedforward Networks with Random H…→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Understanding deep learning (still) requires rethinking generalization→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Fuzzy logic systems for engineering: a tutorial→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Wavelet networks→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Extreme learning machines: a survey→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Nonlinear black-box modeling in system identification: a unified overview→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Review of Deep Learning Algorithms and Architectures→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Neural networks for control systems—A survey→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Approximation theory of the MLP model in neural networks→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Approximation by superpositions of a sigmoidal function→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Approximation by superpositions of a sigmoidal function→Parallel Distributed ProcessingLegacy edge — source detail unavailable
• Approximation by superpositions of a sigmoidal function→An introduction to computing with neural netsLegacy edge — source detail unavailable
• Approximation by superpositions of a sigmoidal function→On the approximate realization of continuous mappings by neural networksLegacy edge — source detail unavailable
• Approximation by superpositions of a sigmoidal function→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Neural networks for control systems—A survey→On the approximate realization of continuous mappings by neural networksLegacy edge — source detail unavailable
• Neural networks for control systems—A survey→An introduction to computing with neural netsLegacy edge — source detail unavailable
• Neural networks for control systems—A survey→30 years of adaptive neural networks: perceptron, Madaline, and backpropagationLegacy edge — source detail unavailable
• Neural networks for control systems—A survey→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• On the approximate realization of continuous mappings by neural networks→An introduction to computing with neural netsLegacy edge — source detail unavailable
• Approximation capabilities of multilayer feedforward networks→On the approximate realization of continuous mappings by neural networksLegacy edge — source detail unavailable
• Approximation capabilities of multilayer feedforward networks→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Extreme learning machines: a survey→On the approximate realization of continuous mappings by neural networksLegacy edge — source detail unavailable
• Extreme learning machines: a survey→Approximation capabilities of multilayer feedforward networksLegacy edge — source detail unavailable
• Extreme learning machines: a survey→Universal Approximation Using Radial-Basis-Function NetworksLegacy edge — source detail unavailable
• Extreme learning machines: a survey→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Extreme learning machines: a survey→Universal Approximation using Incremental Constructive Feedforward Networks with Random H…Legacy edge — source detail unavailable
• Extreme learning machines: a survey→Universal approximation bounds for superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Fuzzy logic systems for engineering: a tutorial→Fuzzy basis functions, universal approximation, and orthogonal least-squares learningLegacy edge — source detail unavailable
• Fuzzy logic systems for engineering: a tutorial→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Fuzzy logic systems for engineering: a tutorial→Orthogonal least squares learning algorithm for radial basis function networksLegacy edge — source detail unavailable
• Nonlinear black-box modeling in system identification: a unified overview→Orthogonal least squares learning algorithm for radial basis function networksLegacy edge — source detail unavailable
• Nonlinear black-box modeling in system identification: a unified overview→Universal approximation bounds for superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Nonlinear black-box modeling in system identification: a unified overview→Wavelet networksLegacy edge — source detail unavailable
• Generating fuzzy rules by learning from examples→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→On the approximate realization of continuous mappings by neural networksLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→An introduction to computing with neural netsLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Fuzzy basis functions, universal approximation, and orthogonal least-squares learning→Generating fuzzy rules by learning from examplesLegacy edge — source detail unavailable
• Fuzzy basis functions, universal approximation, and orthogonal least-squares learning→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Fuzzy basis functions, universal approximation, and orthogonal least-squares learning→Orthogonal least squares learning algorithm for radial basis function networksLegacy edge — source detail unavailable
• An introduction to computing with neural nets→Parallel Distributed ProcessingLegacy edge — source detail unavailable
• Universal Approximation Using Radial-Basis-Function Networks→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation→An introduction to computing with neural netsLegacy edge — source detail unavailable
• 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation→Parallel Distributed ProcessingLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Approximation capabilities of multilayer feedforward networksLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Neural networks for classification: a survey→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Neural networks for classification: a survey→Wavelet networksLegacy edge — source detail unavailable
• Universal Approximation using Incremental Constructive Feedforward Networks with Random H…→On the approximate realization of continuous mappings by neural networksLegacy edge — source detail unavailable
• Universal Approximation using Incremental Constructive Feedforward Networks with Random H…→Approximation capabilities of multilayer feedforward networksLegacy edge — source detail unavailable
• Universal Approximation using Incremental Constructive Feedforward Networks with Random H…→Universal Approximation Using Radial-Basis-Function NetworksLegacy edge — source detail unavailable
• Universal Approximation using Incremental Constructive Feedforward Networks with Random H…→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Universal Approximation using Incremental Constructive Feedforward Networks with Random H…→Universal approximation bounds for superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Orthogonal least squares learning algorithm for radial basis function networks→On the approximate realization of continuous mappings by neural networksLegacy edge — source detail unavailable
• Approximation theory of the MLP model in neural networks→On the approximate realization of continuous mappings by neural networksLegacy edge — source detail unavailable
• Approximation theory of the MLP model in neural networks→Approximation capabilities of multilayer feedforward networksLegacy edge — source detail unavailable
• Approximation theory of the MLP model in neural networks→An introduction to computing with neural netsLegacy edge — source detail unavailable
• Approximation theory of the MLP model in neural networks→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Approximation theory of the MLP model in neural networks→Universal approximation bounds for superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Universal approximation bounds for superpositions of a sigmoidal function→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Wavelet networks→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Wavelet networks→Universal approximation bounds for superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Review of Deep Learning Algorithms and Architectures→Approximation capabilities of multilayer feedforward networksLegacy edge — source detail unavailable