Neural networks for control systems—A survey

Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

• End-to-end training of deep visuomotor policies→Neural networks for control systems—A surveyLegacy edge — source detail unavailable
• Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates→Neural networks for control systems—A surveyLegacy edge — source detail unavailable
• End-to-End Training of Deep Visuomotor Policies→Neural networks for control systems—A surveyLegacy edge — source detail unavailable
• Neural networks for control systems—A survey→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Neural networks for control systems—A survey→Learning Internal Representations by Error PropagationLegacy 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 control systems—A survey→The perceptron: A probabilistic model for information storage and organization in the bra…Legacy edge — source detail unavailable
• Neural networks for control systems—A survey→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Neural networks for control systems—A survey→Identification and control of dynamical systems using 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→Backpropagation through time: what it does and how to do itLegacy edge — source detail unavailable
• Neural networks for control systems—A survey→A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksLegacy 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→Fast Learning in Networks of Locally-Tuned Processing UnitsLegacy edge — source detail unavailable
• Neural networks for control systems—A survey→Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive NetworksLegacy edge — source detail unavailable
• Neural networks for control systems—A survey→Parallel Distributed ProcessingLegacy edge — source detail unavailable
• Neural networks for control systems—A survey→Absolute stability of global pattern formation and parallel memory storage by competitive…Legacy 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→A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)Legacy edge — source detail unavailable
• Neural networks for control systems—A survey→Forward Models: Supervised Learning with a Distal TeacherLegacy 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
• On the approximate realization of continuous mappings by neural networks→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• A Learning Algorithm for Continually Running Fully Recurrent Neural Networks→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• Forward Models: Supervised Learning with a Distal Teacher→Identification and control of dynamical systems using neural networksLegacy edge — source detail unavailable
• Forward Models: Supervised Learning with a Distal Teacher→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• Forward Models: Supervised Learning with a Distal Teacher→Parallel Distributed ProcessingLegacy edge — source detail unavailable
• An introduction to computing with neural nets→Absolute stability of global pattern formation and parallel memory storage by competitive…Legacy 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→An introduction to computing with neural netsLegacy edge — source detail unavailable
• Approximation by superpositions of a sigmoidal function→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation→A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)Legacy edge — source detail unavailable
• 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation→A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksLegacy 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→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation→Identification and control of dynamical systems using neural networksLegacy edge — source detail unavailable
• 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation→Parallel Distributed ProcessingLegacy edge — source detail unavailable
• Multilayer feedforward networks are universal approximators→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Multilayer feedforward networks are universal approximators→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Identification and control of dynamical systems using neural networks→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Identification and control of dynamical systems using neural networks→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Backpropagation through time: what it does and how to do it→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• End-to-End Training of Deep Visuomotor Policies→End-to-end training of deep visuomotor policiesLegacy edge — source detail unavailable
• Fast Learning in Networks of Locally-Tuned Processing Units→An introduction to computing with neural netsLegacy edge — source detail unavailable
• Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates→End-to-End Training of Deep Visuomotor PoliciesLegacy edge — source detail unavailable
• Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates→End-to-end training of deep visuomotor policiesLegacy edge — source detail unavailable