Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm

Lee, Cheng-Ming; Ko, Chia-Nan

doi:10.1016/j.neucom.2009.07.005

Cited by 171 publications

(63 citation statements)

References 30 publications

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“…Some of the earliest research was conducted by Weigend et al (1990), Lowe (1994), Tamiz et al (1996), and Omran (1997). More recent research conducted by Lee and Ko (2009) focuses on Radial Basis Function (RBF) NNs. Lee and Ko (2009) proposed a NTVE-PSO method which compares existing PSO methods, in terms of prediction the different practical load types of Taiwan power system (Taipower) in terms of predicting one-day ahead and five-days ahead.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling and trading the English stock market with novelty optimization techniques

Karathanasopoulos

2016

Econom. bus. letters

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The motivation of this paper is to introduce a short term adaptive model (Partial Swarm Optimizer) combined with linear and nonlinear models when applied to the task of forecasting and trading the daily closing returns of the FTSE100 exchange traded funds (ETFs). This is done by benchmarking its results with a higher order neural network (HONN), a recurrent neural network (RNN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD), plus a buy and hold strategy. More specifically, the trading performance of all models is investigated in forecast and trading simulations on the FTSE 100 ETF time series over the period January 2000 to June 2016 using the last two years for out-of-sample testing. As it turns out, the proposed adaptive models do remarkably well and outperform its benchmarks in terms of correct directional change and trading performance.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling and trading the English stock market with novelty optimization techniques

Karathanasopoulos

2016

Econom. bus. letters

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“…− was defined by Equation (13). To measure the response of the optimal operating point of the PID controller in the drive train system, the SIWPSO Figure 9.…”

Section: Stability Region and Optimal P I D K K K − − Operating Regiomentioning

confidence: 99%

“…This network can determine suitable PI gains according to the reference structure and variable wind speeds. In [13], an nonlinear time-varying evolution PSO (NTVE-PSO) technique was used as a training phase in an RBFNN to optimize the parameters of time-series predictions for various electrical models. The algorithm reported in [14] combined a fuzzy neural network and PSO, designing a controller to adjust the speed of wind energy conversion systems.…”

Section: Introductionmentioning

confidence: 99%

Optimal PID Controller Design Based on PSO-RBFNN for Wind Turbine Systems

2014

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Abstract:A strategy was proposed to determine the optimal operating point for the proportional-integral-derivative (PID) controller of a wind turbine, and identify the stability regions in the parameter space. The proposed approach combined particle swarm optimization (PSO) and radial basis function neural network (RBFNN) algorithms. These intelligent algorithms are artificial learning mechanisms that can determine the optimal operating points, and were used to generate the function representing the most favorable operating p i k k − parameters from each parameter of d k for the stability region of the PID controller. A graphical method was used to determine the 2D or 3D vision boundaries of the PID-type controller space in closed-loop wind turbine systems. The proposed techniques were demonstrated using simulations of a drive train model without time delay and a pitch control model with time delay. Finally, the 3D stability boundaries were determined the proposed graphical approach with and without time delay systems.

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“…The most popular approach for DOS/DDoS attacks prediction is using Artificial Neural Networks (ANNs) classification [28,29,30]. ANNs have become one of the most vital and valuable tools in solving many complex practical problems [31,32], among which the Radial basis function (RBF) neural networks have been successfully applied for solving dynamic system problems, because they can predict the behavior directly from input/output data [33,34,35]. RBF networks have many remarkable characteristics, such as simple network structure, strong learning capacity, better approximation capacities and fast learning speed.…”

Section: Introductionmentioning

confidence: 99%

A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

Karami

Zapata

2015

Neurocomputing

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Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based (host-centric) Internet infrastructure. NDN can overcome the fundamental limitations of the current Internet, in particular, Denial-of-Service (DoS) attacks. However, NDN can be subject to new type of DoS attacks namely Interest flooding attacks and content poisoning. These types of attacks exploit key architectural features of NDN. This paper presents a new intelligent hybrid algorithm for proactive detection of DoS attacks and adaptive mitigation reaction in NDN. In the detection phase, a combination of multiobjective evolutionary optimization algorithm with PSO in the context of the RBF neural network has been applied in order to improve the accuracy of DoS attack prediction. Performance of the proposed hybrid approach is also evaluated successfully by some benchmark problems. In the adaptive reaction phase, we introduced a framework for mitigating DoS attacks based on the misbehaving type of network nodes. The evaluation through simulations shows that the proposed intelligent hybrid algorithm (proactive detection and adaptive reaction) can quickly and effectively respond and mitigate DoS attacks in adverse conditions in terms of the applied performance criteria.

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Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm

Cited by 171 publications

References 30 publications

Modelling and trading the English stock market with novelty optimization techniques

Modelling and trading the English stock market with novelty optimization techniques

Optimal PID Controller Design Based on PSO-RBFNN for Wind Turbine Systems

A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking

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