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Evolving Dynamical Neural Networks for Adaptive Behavior
 Adaptive Behavior
, 1992
"... We would like the behavior of the artificial agents that we construct to be as welladapted to their environments as natural animals are to theirs. Unfortunately, designing controllers with these properties is a very difficult task. In this article, we demonstrate that continuoustime recurrent neur ..."
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Cited by 308 (22 self)
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neural networks are a viable mechanism for adaptive agent control and that the genetic algorithm can be used to evolve effective neural controllers. A significant advantage of this approach is that one need specify only a measure of an agent’s overall performance rather than the precise motor output
Stability Analysis of Dynamical Neural Networks
 IEEE Tansactions on Neural Networks
, 1996
"... In this paper, we use the matrix measure technique to study stability of dynamical neural nctworks. Testable conditions for global exponential stability of nonlinear dynamical systems and dynamical neural networks are given. It shows how a few wellknown results can be unified and generalized in a s ..."
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Cited by 18 (1 self)
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In this paper, we use the matrix measure technique to study stability of dynamical neural nctworks. Testable conditions for global exponential stability of nonlinear dynamical systems and dynamical neural networks are given. It shows how a few wellknown results can be unified and generalized in a
A Dynamic Neural Network for Continual
"... This paper presents a Dynamic Neural Network for learning and classifying dynamic data sets. A brief survey of Dynamic Neural Networks is given, before a definition is ventured. The novel algorithm given here is the Plastic Self Organising Map, a neural network that has the ability to change its own ..."
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This paper presents a Dynamic Neural Network for learning and classifying dynamic data sets. A brief survey of Dynamic Neural Networks is given, before a definition is ventured. The novel algorithm given here is the Plastic Self Organising Map, a neural network that has the ability to change its
Dynamical Neural Networks: . . .
, 2007
"... Our goal is to understand the dynamics of neural computations in lowlevel vision. We study how the substrate of this system, that is local biochemical neural processes, could combine to give rise to an efficient and global perception. We will study these neural computations at different scales fr ..."
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Our goal is to understand the dynamics of neural computations in lowlevel vision. We study how the substrate of this system, that is local biochemical neural processes, could combine to give rise to an efficient and global perception. We will study these neural computations at different scales
TimeIntegration of Dynamical Neural Networks by Composition Methods
, 1997
"... Composition methods are methods for the integration of ordinary differential equations arising from Lie algebra theory: We apply them here to dynamical neural networks. In this method, we split the vector field of the dynamical neural network into the contribution of each of its neurons. We then sol ..."
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Cited by 2 (2 self)
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Composition methods are methods for the integration of ordinary differential equations arising from Lie algebra theory: We apply them here to dynamical neural networks. In this method, we split the vector field of the dynamical neural network into the contribution of each of its neurons. We
Learning Methods for Dynamic Neural Networks
"... AbstractIn the framework of dynamic neural networks, learning refers to the slow process by which a neural network modifies its own structure under the influence of environmental pressure. Our simulations take place on large random recurrent neural networks (RRNNs). We present several results obta ..."
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Cited by 2 (0 self)
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AbstractIn the framework of dynamic neural networks, learning refers to the slow process by which a neural network modifies its own structure under the influence of environmental pressure. Our simulations take place on large random recurrent neural networks (RRNNs). We present several results
Composition Methods for the Integration of Dynamical Neural Networks
, 1996
"... We apply the symmetric composition method for the integration of ordinary differential equations to dynamical neural networks. In this method, we split the vector field, which is parameterized by a neural network, into the contribution of each of its neurons. We then solve the elementary differenti ..."
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We apply the symmetric composition method for the integration of ordinary differential equations to dynamical neural networks. In this method, we split the vector field, which is parameterized by a neural network, into the contribution of each of its neurons. We then solve the elementary
A dynamic neural network for syllable recognition
 In Proc. Int. Joint Conf. Neural Networks
, 1999
"... A dynamic neural network architecture based on the TimeDelay Neural Network and the Convolutional Neural Network is originated. The dynamic network achieves much better performance than those of MLP and TDNN when dealing with syllable recognition. Such performance is also comparable to that of the ..."
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Cited by 3 (2 self)
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A dynamic neural network architecture based on the TimeDelay Neural Network and the Convolutional Neural Network is originated. The dynamic network achieves much better performance than those of MLP and TDNN when dealing with syllable recognition. Such performance is also comparable
Sequential Behavior and Learning in Evolved Dynamical Neural Networks
, 1994
"... This paper explores the use of a realvalued modular genetic algorithm to evolve continuoustime recurrent neural networks capable of sequential behavior and learning. We evolve networks that can generate a fixed sequence of outputs in response to an external trigger occurring at varying intervals o ..."
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Cited by 58 (3 self)
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assume neither an a priori discretization of states or time nor an a priori learning algorithm that explicitly modifies network parameters during learning. Rather, we merely expose dynamical neural networks to tasks that require sequential behavior and learning and allow the genetic algorithm to evolve
Dynamical Neural Networks for Mobile Robot Control
 NRL Memorandum Report AIC03393 (Naval Research Laboratory
, 1993
"... This paper describes research in applying dynamical neural networks (DNNs) to the control of real mobile robots. Three tasks were explored: predator avoidance, reactive navigation, and landmark recognition. For the predator avoidance and landmark recognition tasks, a genetic algorithm was used to ev ..."
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Cited by 10 (0 self)
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This paper describes research in applying dynamical neural networks (DNNs) to the control of real mobile robots. Three tasks were explored: predator avoidance, reactive navigation, and landmark recognition. For the predator avoidance and landmark recognition tasks, a genetic algorithm was used
Results 1  10
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100,752