Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

Da, Feipeng

doi:10.1007/s00521-007-0130-x

Cited by 12 publications

(7 citation statements)

References 30 publications

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“…New neurons in rule layer are dynamically created and adapted depending on the input data. [18] firstly introduced the fuzzy neural network to predict delayed time for process control. Then as presented in [19], an adaptive neuro-fuzzy inference system (ANFIS), which integrates neural network with fuzzy inference system, was deployed to control the speed of a heavy duty vehicle.…”

Section: Fuzzy Logic and Evolving Fuzzy Neural Network (Efunn)mentioning

confidence: 99%

Effective long-term travel time prediction with fuzzy rules for tollway

Li¹,

Rose

Chen

et al. 2017

Neural Comput & Applic

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Abstract:Advanced traveller information system is an important Intelligent Transportation Systems (ITS) application area, which provides information to transport users and managers in order to improve the efficiency and effectiveness of the transportation system, in the face of increasing congestion in urban cities around the world. So far very limited research attention has been focused on long term travel time prediction (i.e. predicting greater than 60 minutes ahead). Long term travel time forecasts can play a critical role in journey planning decisions for both private road users and logistics operators. In this paper, we have considered a fuzzy neural network incorporated with both imprecise and numerical information and developed a hybrid long term travel time prediction model, which shows the better prediction capability than naive methods and highlights the importance of different data variables. The model combines the learning ability of neural networks and the knowledge extraction ability of fuzzy inference systems. The model was validated by using travel time data compiled from electronic toll tags on a 14 km length section of the CityLink tollway in Melbourne, Australia. The validation results highlight the ability of the fuzzy neural network model to accommodate imprecise and linguistic input information, while providing reliable predictions of travel times up to a few days ahead.

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Section: Fuzzy Logic and Evolving Fuzzy Neural Network (Efunn)mentioning

confidence: 99%

Effective long-term travel time prediction with fuzzy rules for tollway

Li¹,

Rose

Chen

et al. 2017

Neural Comput & Applic

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“…In SMC, the sliding condition is derived as σ T (t)σ(t) < 0 such that the stability can be guaranteed for the closed-loop system [2]. In order to train the WNN effectively, the online parameter learning algorithm is a gradient descent method that aims to minimize σ T (t)σ(t) for achieving fast convergence of σ(t).…”

Section: Online Adaptation Laws For Wnnmentioning

confidence: 99%

Design of Active Heat Dissipation System for Adaptive Wavelet Neural Network Control

Chen¹,

Lee²,

Hsu³

et al. 2014

NNW

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This tutorial is based on modification of the professor nomination lecture presented two years ago in front of the Scientific Council of the Czech Technical University in Prague [16].It is devoted to the techniques for the models developing suitable for processes forecasting in complex systems. Because of the high sensitivity of the processes to the initial conditions and, consequently, due to our limited possibilities to forecast the processes for the long-term horizon, the attention is focused on the techniques leading to practical applications of the short term prediction models. The aim of this tutorial paper is to bring attention to possible difficulties which designers of the predicting models and their users meet and which have to be solved during the prediction model developing, validation, testing, and applications. The presented overview is not complete, it only reflects the author's experience with developing of the prediction models for practical tasks solving in banking, meteorology, air pollution and energy sector. The paper is completed by an example of the global solar radiation prediction which forms an important input for the electrical energy production forecast from renewable sources. The global solar radiation forecasting is based on numerical weather prediction models. The time-lagged ensemble technique for uncertainty quantification is demonstrated on a simple example.

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“…During the past few years, neural-network-based feedback control technique has attracted increasing attentions, because it has provided an efficient and effective way for controlling the complex nonlinear or ill-defined systems [1][2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the parameterized NNs can approximate any unknown system dynamics or the ideal tracking controller with arbitrary degree of accuracy after learning. Moreover, according to the structure, NNs can be mainly classified as feedforward neural networks (FNNs) [1][2][3][4][5] and recurrent neural networks (RNNs) [6][7][8][9]. As known, FNNs represent a static mapping network.…”

Section: Introductionmentioning

confidence: 99%

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Recurrent wavelet neural backstepping controller design with a smooth compensator

Chen

Hsu

2010

Neural Comput & Applic

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Recurrent wavelet neural network (RWNN) has the advantages in its dynamic responses and information storing ability. This paper develops a recurrent wavelet neural backstepping control (RWNBC) scheme for multiple-input multiple-output (MIMO) mechanical systems. This proposed RWNBC comprises a neural controller and a smooth compensator. The neural controller using an RWNN is the principal tracking controller utilized to mimic an ideal backstepping control law; and the parameters of RWNN are online tuned by the derived adaptation laws from the Lyapunov stability theorem. The smooth compensator is designed to dispel the approximation error introduced by the neural controller, so that the asymptotic stability of the closed-loop system can be guaranteed. Finally, two MIMO mechanical systems, a mass-springdamper system and a two-inverted pendulum system, are performed to verify the effectiveness of the proposed RWNBC scheme. From the simulation results, it is verified that the proposed RWNBC scheme can achieve favorable tracking performance without any chattering phenomenon.

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Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

Cited by 12 publications

References 30 publications

Effective long-term travel time prediction with fuzzy rules for tollway

Effective long-term travel time prediction with fuzzy rules for tollway

Design of Active Heat Dissipation System for Adaptive Wavelet Neural Network Control

Recurrent wavelet neural backstepping controller design with a smooth compensator

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