Journal ArticleDOI
The effect of different basis functions on a radial basis function network for time series prediction: A comparative study
C. Harpham,Christian W. Dawson +1 more
TLDR
The results indicate that the choice of basis function (and, where appropriate, basis width parameter) is data set dependent and evaluating all recognised basis functions suitable for RBF networks is advantageous.About:
This article is published in Neurocomputing.The article was published on 2006-10-01. It has received 154 citations till now. The article focuses on the topics: Radial basis function network & Basis function.read more
Citations
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Journal ArticleDOI
Machine learning for estimation of building energy consumption and performance: a review
TL;DR: A substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance are provided.
Journal ArticleDOI
Fuzzy Wavelet Neural Network Models for Prediction and Identification of Dynamical Systems
Sevcan Yilmaz,Yusuf Oysal +1 more
TL;DR: The proposed FWNN models are obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with wavelet basis functions that have the ability to localize both in time and frequency domains.
Journal ArticleDOI
Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm
Cheng-Ming Lee,Chia-Nan Ko +1 more
TL;DR: Simulation results illustrate that the proposed NTVE-PSO-RBFNN has better forecasting accuracy and computational efficiency for different electricity demands than the other PSO- RBFNNs.
Journal ArticleDOI
A Review of Deep Learning Models for Time Series Prediction
TL;DR: This paper reviews the state of the art developments in deep learning for time series prediction and categorizes them into discriminative, generative, and hybrids models, based on modeling for the perspective of conditional or joint probability.
Journal ArticleDOI
Developing a Local Least-Squares Support Vector Machines-Based Neuro-Fuzzy Model for Nonlinear and Chaotic Time Series Prediction
TL;DR: The promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series is demonstrated.
References
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Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more