Memetic cooperative coevolution of Elman recurrent neural networks

Chandra, Rohitash

doi:10.1007/s00500-013-1160-1

Cited by 9 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, radial quantity function (RBF) [14,15], support vector machine (SVM) [16], least squares support vector machine (LS-SVM) [17], backpropagation neural network (BPNN) [13], fuzzy neural network (FNN) [18], recursive neural network (RNN) [19,20], multilayer perceptron (MLP), nonlinear regression model (NAR), recursive predictor (RPNN), and ESN [21]. Chandra used the method of memetic cooperative coevolution to train the Elman recurrent neural network on the problem of grammatical inference [22]. Two years later, Chandra used Elman neural network to characterize chaotic time series and added cooperation and competition into them, so as to achieve the purpose of prediction [20].…”

Section: Introductionmentioning

confidence: 99%

Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network

Ling

2020

Complexity

View full text Add to dashboard Cite

In target estimating sea clutter or actual mechanical fault diagnosis, useful signal is often submerged in strong chaotic noise, and the targeted signal data are difficult to recover. Traditional schemes, such as Elman neural network (ENN), backpropagation neural network (BPNN), support vector machine (SVM), and multilayer perceptron- (MLP-) based model, are insufficient to extract the weak signal embedded in a chaotic background. To improve the estimating accuracy, a novel estimating method for aiming at extracting problem of weak pulse signal buried in a strong chaotic background is presented. Firstly, the proposed method obtains the vector sequence signal by reconstructing higher-dimensional phase space data matrix according to the Takens theorem. Then, a Jordan neural network- (JNN-) based model is designed, which can minimize the error squared sum by mixing the single-point jump model for targeting signal. Finally, based on short-term predictability of chaotic background, estimation of weak pulse signal from the chaotic background is achieved by a profile least square method for optimizing the proposed model parameters. The data generated by the Lorenz system are used as chaotic background noise for the simulation experiment. The simulation results show that Jordan neural network and profile least square algorithm are effective in estimating weak pulse signal from chaotic background. Compared with the traditional method, (1) the presented method can estimate the weak pulse signal in strong chaotic noise under lower error than ENN-based, BPNN-based, SVM-based, and -ased models and (2) the proposed method can extract the weak pulse signal under a higher output SNR than BPNN-based model.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network

Ling

2020

Complexity

View full text Add to dashboard Cite

show abstract

“…However, despite the success and simplicity of CGA, competitive predatorprey systems have seldom been used for ANN training. Indeed, to date most co-evolutionary methods are based on cooperative procedures, where separate populations of components learn to collaborate like symbiotic organisms to solve a task (Potter and Jong 2000;García-Pedrajas et al 2003;Gomez et al 2008;Chandra 2013;Rivera et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Competitive co-evolution of multi-layer perceptron classifiers

Castellani

2017

Soft Comput

View full text Add to dashboard Cite

This paper analyses the competitive approach to the co-evolutionary training of multi-layer perceptron classifiers. Two algorithms were tested: the first opposes a population of classifiers to a population of training patterns; the second pits a population of classifiers against a population of subsets of training patterns. The classifiers are regarded as predators that need to 'capture' (correctly categorise) the prey (training patterns). Success for the predators is measured on their ability to capture prey. Success for the prey is measured on their ability to escape predation (be misclassified). The aim of the procedure is to create an evolutionary tug-of-war between the best classifiers and the most difficult data samples, increasing the efficiency and accuracy of the learning process. The two co-evolutionary algorithms were tested on a number of well-known benchmarks and on several artificial data sets modelling different kinds of common classification problems such as overlapping data categories, noisy training inputs, and unbalanced data classes. The performance of the co-evolutionary methods was compared with that of two traditional training techniques: the standard backpropagation rule and a conventional evolutionary algorithm. The co-evolutionary procedures achieved top accuracy in all classification problems. They particularly excelled on data sets containing noisy training inputs, where they outperformed the backpropagation rule, and on tasks involving unbalanced data classes, where they outperformed both backpropagation and the conventional evolutionary algorithm. Compared to the standard evolutionary algorithm, the co-evolutionary procedures were able to obtain similar or superior learning accuracies, whilst needing considerably less presentations of the training patterns. This economy in the use of training patterns translated into significant savings in computational overheads and algorithms running time.

show abstract

Dynamics

Minati¹,

Pessa²

2018

From Collective Beings to Quasi-Systems

View full text Add to dashboard Cite

Memetic cooperative coevolution of Elman recurrent neural networks

Cited by 9 publications

References 34 publications

Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network

Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network

Competitive co-evolution of multi-layer perceptron classifiers

Dynamics

Contact Info

Product

Resources

About