Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks

Sadiq, Alishba; Ibrahim, Muhammad Sohail; Usman, Muhammad; Zubair, Muhammad; Khan, Shujaat

doi:10.1109/iceest.2018.8643321

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…A chaotic time series can be used for short-term prediction but not long-term prediction. As a three-layer feedforward neural network, the RBF neural network has the advantages of global convergence, straightforward determination of the network structure, and quick training process, and it is effective in complex time-series predictions [31,32]. The local nonlinear method can be used to reduce noise [26,33,34], and we tested its prediction ability.…”

Section: Chaotic One-step Predictionmentioning

confidence: 99%

Research on the Chaotic Characteristics and Noise Reduction Prediction of Information System Anomalies in Equipment Manufacturing Enterprises

2021

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As the process of informatization progresses in an equipment manufacturing enterprise, its information system becomes a dissipative structure due to the nonlinear interaction of many factors. The objectives of this study were to help enterprises adopt intelligent manufacturing, realize sustainable development strategies, and understand the operation rules of information systems. For this purpose, this study analyzed an anomaly index time series of an information system in the process of integration. First, the embedding dimension, time delay, average period, and maximum Lyapunov exponent of the time series were calculated. The anomaly index with chaotic characteristics was denoised by combining phase space reconstruction with singular value decomposition (SVD). Finally, a radial basis function (RBF) neural network and local nonlinear method were used to predict the anomaly index of 79 test data points. The case simulation results verify that the anomaly index is affected by changes in basic data, system development, and online migration. One instance of local noise reduction can reveal hidden problems in the actual operations of enterprises, and multiple iterations can extract the actual information of the signals, avoid failures at isolated points, and show a clear attractor structure. Both the RBF neural network and local nonlinear approach are effective prediction methods with low relative errors, but the performance of the latter is superior.

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Section: Chaotic One-step Predictionmentioning

confidence: 99%

Research on the Chaotic Characteristics and Noise Reduction Prediction of Information System Anomalies in Equipment Manufacturing Enterprises

2021

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“…Machine learning (ML) is an established field with a wide range of applications including control engineering [5,18,24,29], medical imaging [23,35,47], bioinformatics [26,31,41], and design of forecasting systems [11,19,36,48], etc. It has been successfully used for other innovative applications as well such as in the design of cognitive communication systems [6,34] and powerful generative models for number of multimedia application [13,27] .…”

Section: Introductionmentioning

confidence: 99%

“…This hybrid approach for RBF outperformed conventional non-hybrid approaches. Another emerging variant of RBFNN called spatio-temporal RBFNN, uses the concept of time-space orthogonality to separately model the dynamics and nonlinear complexities [20,36]. Additionally, an adaptive Nelder Mead Simplex [12], based training method that simultaneously updates weights and kernel width is proposed in [15].…”

Section: Introductionmentioning

confidence: 99%

Multi-Kernel Fusion for RBF Neural Networks

Atif

Khan

Naseem

et al. 2022

Neural Process Lett

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A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generation of radial basis function neural network is equipped with multiple kernels which provide significant performance benefits compared to the previous generation using only a single kernel. In existing multi-kernel RBF algorithms, multi-kernel is formed by the convex combination of the base/primary kernels. In this paper, we propose a novel multi-kernel RBFNN in which every base kernel has its own (local) weight. This novel flexibility in the network provides better performance such as faster convergence rate, better local minima and resilience against stucking in poor local minima. These performance gains are achieved at a competitive computational complexity compared to the contemporary multi-kernel RBF algorithms. The proposed algorithm is thoroughly analysed for performance gain using mathematical and graphical illustrations and also evaluated on three different types of problems namely: (i) pattern classification, (ii) system identification and (iii) function approximation. Empirical results clearly show the superiority of the proposed algorithm compared to the existing state-of-the-art multi-kernel approaches.

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“…This hybrid approach for RBF outperformed conventional nonhybrid approaches. Another emerging variant of RBFNN called spatio-temporal RBFNN, uses the concept of time-space orthogonality to separately model the dynamics and nonlinear complexities [20,36]. Additionally, an adaptive Nelder Mead Simplex [12], based training method that simultaneously updates weights and kernel width is proposed in [15].…”

Section: Introductionmentioning

confidence: 99%