Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction

Whitehead, Bruce A.; Choate, T.D.

doi:10.1109/72.508930

Cited by 208 publications

(76 citation statements)

References 53 publications

(4 reference statements)

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“…The Mackey Glass time series prediction. This time series is widely regarded as a benchmark for comparing the general ization ability of RBNN [9,17,7,16]. It is a chaotic time series created by the Mackey Glass delay difference equation [6]:…”

Section: Domains Descriptionmentioning

confidence: 99%

Learning radial basis neural networks in a lazy way: A comparative study

2008

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a b s t r a c tLazy learning methods have been used to deal with problems in which the learning examples are not evenly distributed in the input space. They are based on the selection of a subset of training patterns when a new query is received. Usually, that selection is based on the k closest neighbors and it is a static selection, because the number of patterns selected does not depend on the input space region in which the new query is placed. In this paper, a lazy strategy is applied to train radial basis neural networks. That strategy incorporates a dynamic selection of patterns, and that selection is based on two different kernel functions, the Gaussian and the inverse function. This lazy learning method is compared with the classical lazy machine learning methods and with eagerly trained radial basis neural networks.

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Section: Domains Descriptionmentioning

confidence: 99%

Learning radial basis neural networks in a lazy way: A comparative study

2008

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“…where α = 0.2, β = −0.1, τ = 17 as used in [7,8,[13][14][15]. Each network receives four past data points x(t), x(t − 6), x(t − 12) and x(t − 18) as inputs and predicts 6 time steps ahead (x(t + 6)).…”

Section: Mackey-glass Time Series Prediction Problemmentioning

confidence: 99%

“…As discussed in [7], while evolving a single network we should have β ∈ (1, 2) to encourage the desired behaviour of competition and cooperation among RBGFs with similar and different functionalities, respectively. Also, β = 2.0 produces too much of competition and β = 1.0 is insufficient to prevent competition among RBGFs with dissimilar activations, while β = 1.5 yields the desired behavior.…”

Section: Weights-based Credit Assignmentmentioning

confidence: 99%

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Credit Assignment Among Neurons in Co-evolving Populations

Khare

Yao

Sendhoff

2004

Lecture Notes in Computer Science

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Abstract. Different credit assignment strategies are investigated in a two level co-evolutionary model which involves a population of Gaussian neurons and a population of radial basis function networks consisting of neurons from the neuron population. Each individual in neuron population can contribute to one or more networks in network population, so there is a two-fold difficulty in evaluating the effectiveness (or fitness) of a neuron. Firstly, since each neuron only represents a partial solution to the problem, it needs to be assigned some credit for the complete problem solving activity. Secondly, these credits need to be accumulated from different networks the neuron participates in. This model, along with various credit assignment strategies, is tested on a classification (Heart disease diagnosis problem from UCI machine learning repository) and a regression problem (Mackey-Glass time series prediction problem).

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“…The overall efficiency of RBFNs has been proved in many areas such as pattern classification [6], function approximation [18] or time series prediction [25]. Typically these networks are design by means of evolutionary algorithms [11].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Systems in Long-Term Forecasting of the Extra-Virgin Olive Oil Price in the Spanish Market

Pérez-Godoy¹,

Pérez²,

Rivera³

et al. 2010

Trends in Applied Intelligent Systems

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Abstract. In this paper the problem of estimating forecasts, for the Official Market of future contracts for olive oil in Spain, is addressed. Time series analysis and their applications is an emerging research line in the Intelligent Systems field. Among the reasons for carry out time series analysis and forecasting, the associated increment in the benefits of the implied organizations must be highlighted. In this paper an adaptation of CO 2 RBFN, evolutionary COoperative-COmpetitive algorithm for Radial Basis Function Networks design, applied to the long-term prediction of the extra-virgin olive oil price is presented. This long-term horizon has been fixed to six months. The results of CO 2 RBFN have been compared with other data mining methods, typically used in time series forecasting, such as other neural networks models, a support vector machine method and a fuzzy system.

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Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction

Cited by 208 publications

References 53 publications

Learning radial basis neural networks in a lazy way: A comparative study

Learning radial basis neural networks in a lazy way: A comparative study

Credit Assignment Among Neurons in Co-evolving Populations

Intelligent Systems in Long-Term Forecasting of the Extra-Virgin Olive Oil Price in the Spanish Market

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