Above are papers related to the following paper: [Paper_ID] Paper #75983. 	[Title] An Analytical Framework for Local Feedforward Networks [Abstract] Interference in neural networks occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are referred to as spatially local networks. To understand these properties, a theoretical framework, consisting of a measure of interference and a measure of network localization, is developed. These measures incorporate not only the network weights and architecture but also the learning algorithm. Using this framework to analyze sigmoidal, multi-layer perceptron (MLP) networks that employ the back-propagation learning algorithm, we address a familiar misconception that single-hidden-layer sigmoidal networks are inherently non-local by demonstrating that given a sufficiently large number of adjustable weights, single-hidden-layer sigmoidal MLPs can be made arbitrarily local while retaining the ability to approximate any continuous function on a compact domain. fl Partially supported under Task 2312 R1 by the United States Air Force Office of Scientific Research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106568. 	[Title] Local Feedforward Networks [Abstract] Although feedforward neural networks are well suited to function approximation, in some applications networks experience problems when learning a desired function. One problem is interference which occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are referred to as spatially local networks. To understand these properties, a theoretical framework, consisting of a measure of interference and a measure of network localization, is developed that incorporates not only the network weights and architecture but also the learning algorithm. Using this framework to analyze sigmoidal multi-layer perceptron (MLP) networks that employ the back-prop learning algorithm, we address a familiar misconception that sigmoidal networks are inherently non-local by demonstrating that given a sufficiently large number of adjustable parameters, sigmoidal MLPs can be made arbitrarily local while retaining the ability to represent any continuous function on a compact domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1115790. 	[Title] Adaptive Wavelet Control of Nonlinear Systems [Abstract] This paper considers the design and analysis of adaptive wavelet control algorithms for uncertain nonlinear dynamical systems. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control scheme based on nonlinearly parametrized wavelet network models. Semi-global stability results are obtained under the key assumption that the system uncertainty satisfies a "matching" condition. The localization properties of adaptive networks are discussed and formal definitions of interference and localization measures are proposed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #227286. 	[Title] Space-Frequency Localized Basis Function Networks for Nonlinear System Estimation and Control [Abstract] Stable neural network control and estimation may be viewed formally as a merging of concepts from nonlinear dynamic systems theory with tools from multivariate approximation theory. This paper extends earlier results on adaptive control and estimation of nonlinear systems using gaussian radial basis functions to the on-line generation of irregularly sampled networks, using tools from multiresolution analysis and wavelet theory. This yields much more compact and efficient system representations while preserving global closed-loop stability. Approximation models employing basis functions that are localized in both space and spatial frequency admit a measure of the approximated function's spatial frequency content that is not directly dependent on reconstruction error. As a result, these models afford a means of adaptively selecting basis functions according to the local spatial frequency content of the approximated function. An algorithm for stable, on-line adaptation of output weights simultaneously with node configuration in a class of non-parametric models with wavelet basis functions is presented. An asymptotic bound on the error in the network's reconstruction is derived and shown to be dependent solely on the minimum approximation error associated with the steady state node configuration. In addition, prior bounds on the temporal bandwidth of the system to be identified or controlled are used to develop a criterion for on-line selection of radial and ridge wavelet basis functions, thus reducing the rate of increase in network's size with the dimension of the state vector. Experimental results obtained by using the network to predict the path of an unknown light bluff object thrown through air, in an active-vision based robotic catching system, are given to illustrate the network's performance in a simple real-time application. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3229. 	[Title] Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2 [Abstract] Neural computation, also called connectionism, parallel distributed processing, neural network modeling or brain-style computation, has grown rapidly in the last decade. Despite this explosion, and ultimately because of impressive applications, there has been a dire need for a concise introduction from a theoretical perspective, analyzing the strengths and weaknesses of connectionist approaches and establishing links to other disciplines, such as statistics or control theory. The Introduction to the Theory of Neural Computation by Hertz, Krogh and Palmer (subsequently referred to as HKP) is written from the perspective of physics, the home discipline of the authors. The book fulfills its mission as an introduction for neural network novices, provided that they have some background in calculus, linear algebra, and statistics. It covers a number of models that are often viewed as disjoint. Critical analyses and fruitful comparisons between these models Please summarise all the given information.

Paper #75983 has the title `An Analytical Framework for Local Feedforward Networks  
` and the abstract `Interference in neural networks occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are referred to as spatially local networks. To understand these properties, a theoretical framework, consisting of a measure of interference and a measure of network localization, is developed. These measures incorporate not only the network weights and architecture but also the learning algorithm. Using this framework to analyze sigmoidal, multi-layer perceptron (MLP) networks that employ the back-propagation learning algorithm, we address a familiar misconception that single-hidden-layer sigmoidal networks are inherently non-local by demonstrating that given a sufficiently large number of adjustable weights, single-hidden-layer sigmoidal MLPs can be made arbitrarily local while retaining the ability to approximate any continuous function on a compact domain. fl Partially supported under Task 2312 R1 by the United States Air Force Office of Scientific Research.
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Question: Which category should Paper #75983 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #75983 has the title `An Analytical Framework for Local Feedforward Networks  
` and the abstract `Interference in neural networks occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are referred to as spatially local networks. To understand these properties, a theoretical framework, consisting of a measure of interference and a measure of network localization, is developed. These measures incorporate not only the network weights and architecture but also the learning algorithm. Using this framework to analyze sigmoidal, multi-layer perceptron (MLP) networks that employ the back-propagation learning algorithm, we address a familiar misconception that single-hidden-layer sigmoidal networks are inherently non-local by demonstrating that given a sufficiently large number of adjustable weights, single-hidden-layer sigmoidal MLPs can be made arbitrarily local while retaining the ability to approximate any continuous function on a compact domain. fl Partially supported under Task 2312 R1 by the United States Air Force Office of Scientific Research.
`.
Question: Which category should Paper #75983 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks