Journal ArticleDOI
Time-series prediction with single integrate-and-fire neuron
Abhishek Yadav,Deepak Mishra,Ram Narayan Yadav,Sudipta Ray,Prem Kalra +4 more
- Vol. 7, Iss: 3, pp 739-745
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TLDR
It is found that a single IFN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network.Abstract:
In this paper, a learning algorithm for a single integrate-and-fire neuron (IFN) is proposed and tested for various applications in which a multilayer perceptron neural network is conventionally used. It is found that a single IFN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Several benchmark and real-life problems of classification and time-series prediction have been illustrated. It is observed that the inclusion of some more biological phenomenon in an artificial neural network can make it more powerful.read more
Citations
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Journal ArticleDOI
PSO-based single multiplicative neuron model for time series prediction
Liang Zhao,Yupu Yang +1 more
TL;DR: An improved version of the original PSO, cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of the conventional PSO and demonstrates the superiority of CRPSO-based neuron model in efficiency and robustness over the other three algorithms.
Journal ArticleDOI
A novel single multiplicative neuron model trained by an improved glowworm swarm optimization algorithm for time series prediction
TL;DR: The novel SMRN model combined with the proposed LWGSODE algorithm provides a promising means to approximate nonlinear series in the future.
Journal ArticleDOI
An improved group search optimizer with operation of quantum-behaved swarm and its application
TL;DR: The improved GSO algorithm (IGSO) is tested on several benchmark functions and applied to train single multiplicative neuron model, and the results of the experiments indicate that IGSO is competitive to some other EAs.
Journal ArticleDOI
A hybrid GMDH and least squares support vector machines in time series forecasting
TL;DR: A novel hybrid forecasting model which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM, is proposed which provides a promising technique in time series forecasting methods.
Journal ArticleDOI
Time series prediction with improved neuro-endocrine model
TL;DR: To indicate the effectiveness of the proposed model, some time series from different research fields, which are used in some literatures, are tested and indicate that the model has some good performance.
References
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A quantitative description of membrane current and its application to conduction and excitation in nerve
A. L. Hodgkin,A. F. Huxley +1 more
TL;DR: This article concludes a series of papers concerned with the flow of electric current through the surface membrane of a giant nerve fibre by putting them into mathematical form and showing that they will account for conduction and excitation in quantitative terms.
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A logical calculus of the ideas immanent in nervous activity
Warren S. McCulloch,Walter Pitts +1 more
TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.
Book
Adaptive Signal Processing
Bernard Widrow,Samuel D. Stearns +1 more
TL;DR: This chapter discusses Adaptive Arrays and Adaptive Beamforming, as well as other Adaptive Algorithms and Structures, and discusses the Z-Transform in Adaptive Signal Processing.