Extrapolation of Mackey-Glass data using Cascade Correlation

Ensley, D. David; Nelson, Dale E.

doi:10.1177/003754979205800506

Cited by 21 publications

(4 citation statements)

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“…O algoritmo original proposto em [8] foi modificado para permitir a utilização de mais de uma função de ativação como candidatos a compor a nova unidade a ser introduzida em cascata [6]. Isso significa que unidades com funções de ativação diferentes podem competir simultaneamente.…”

Section: Funções De Ativação Distintasunclassified

Redes Neurais Construtivas: Uma Abordagem Comparativa

Castro¹,

Iyoda²,

Pinheiro³

et al. 2016

Anais Do 4. Congresso Brasileiro De Redes Neurais

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Until recently, the determination of a proper dimension for an artificial neural network in a given application task usually involved only the designer's experience in implementing trial and error procedures. Nowadays, automatic design of neural network architectures is becoming part of the training process, by means of a more efficient exploration of the available information for supervised learning. Among the already proposed alternatives to automatic design, this paper emphasizes methods founded on the constructive paradigm. We chose three constructive algorithms for comparison: A* heuristic search (A*), Cascade-Correlation with different activation functions (CASCOR) and Projection Pursuit Learning (PPL). A brief review of these constructive strategies in the solution of regression problems is presented, and their performance in terms of the parsimony of the resultant architecture is verified in benchmark problems.

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Section: Funções De Ativação Distintasunclassified

Redes Neurais Construtivas: Uma Abordagem Comparativa

Castro¹,

Iyoda²,

Pinheiro³

et al. 2016

Anais Do 4. Congresso Brasileiro De Redes Neurais

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“…Radial Basis Functions (Wedding & Cios, 1996), 4. Cascade Correlation (Ensley & Nelson, 1992), 5. General Regression Neural Net (Chen, 1992), 6.…”

Section: Types Of Networkmentioning

confidence: 99%

Current Approaches in Neural Network Modeling of Financial Time Series

Singh¹

2009

SSRN Journal

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Neural networks are an artificial intelligence method for modeling complex non-linear functions. Artificial Neural Networks (ANNs) have been widely applied to the domain of prediction problems. Considerable research effort has gone into ANNs for modeling financial time series. This paper attempts to provide an overview of recent research in this area, emphasizing the issues that are particularly important with respect to the neural network approach to financial time series prediction task. The purpose of this paper is to provide (1) a survey of research in this area, and, (2) synthesize insights from published research on ANN modeling issues, specifically, those that have a bearing on the design factors of neural network such as variable selection, data preprocessing, and network architecture. The paptir concludes with a discussion of current and emerging research topics related to neural networks in financial time series prediction.

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“…Several researchers have developed successful neural methods of modeling dynamic processes, though interest has been limited to nonspatially distributed problems. Examples include the simulation of chaotic time series [see Moody and Darken (1989) and Ensley and Nelson (1992)] and the development of a controller to solve the truck back-up problem [see Shelton and Peterson (1992)]. The work presented in this paper moves considerably beyond this, developing a method of modeling poorly understood processes that, in addition to being dynamic, operate over continuous regions of space.…”

Section: Introductionmentioning

confidence: 99%

Simulating the behavior of poorly understood continua using neural networks

Flood

1996

AIEDAM

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This paper proposes and evaluates an artificial neural network based method of modeling the dynamic behavior of spatially distributed continuous engineering processes. The technique is applicable to situations where the differential equations governing the behavior of a system are nonlinear and poorly understood, such as is the case for frost-heave and thaw-settlement processes in soils. A description is first provided of a means of modeling the unknown component of governing differential equations. A range of levels of understanding of these equations is considered. A method of discretizing the resultant neural models of these equations is then illustrated, and the way in which these can be used to simulate the behavior of a process is described. The performance of the proposed neural network approach is then assessed in a series of experiments simulating the nonlinear thermal behavior of translucent solid materials. The system is proven capable of providing highly accurate simulations of system behavior sustained over many thousands of simulation time steps. The paper concludes with an identification of several ongoing areas of further development and application of the proposed tool.

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Extrapolation of Mackey-Glass data using Cascade Correlation

Cited by 21 publications

References 0 publications

Redes Neurais Construtivas: Uma Abordagem Comparativa

Redes Neurais Construtivas: Uma Abordagem Comparativa

Current Approaches in Neural Network Modeling of Financial Time Series

Simulating the behavior of poorly understood continua using neural networks

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