Recurrent Radial Basis Function Network Research Articles

Robust Adaptive Neural Network Tracking Control With Optimized Super-Twisting Sliding-Mode Technique for Induction Motor Drive System

This article proposes a robust adaptive supertwisting sliding-mode controller (STSMC) for high-precision IM drive. First, the STSMC is developed for controlling and stabilizing the IM. However, the control performance may be influenced due to exterior disorders along with parameter variations since the IM model is not accurately known during operation. Therefore, it is essential to estimate the lumped disturbance to strength the robustness of the STSMC using system model-based disturbance estimator. Second, to optimize the control performance, a robust adaptive neural-network tracking controller is developed to evade the constraints on knowing the disorders and uncertainties upper bounds. The RANTC combines an STSMC and a recurrent radial basis function network (RRBFN) estimators. The RRBFNs are employed to approximate the IM dynamics along with estimating the optimum super-twisting control algorithms online. The RRBFN training is developed via adaptive particle swarm optimization (APSO) algorithm to attain enhanced performance in less learning iterations, which is the critical issue in real-time applications. Third, to imitate the STSMC algorithms for preserving the robust control characteristics without the necessity of the IM parameters, a direct adaptive intelligent controller (DAIC) based on Takagi–Sugeno–Kang-type recurrent Petri fuzzy-neural-network is developed. Furthermore, the online adaptive laws are derived according to Lyapunov theory, so that the system stability can be assured. The validity of control schemes is verified by simulation and experimentation. The simulation and experimental results confirm the superiority of the STSMC-SMDE, RANTC, and DAIC schemes compared with STSMC irrespective of exterior disorders along with indefinite model uncertainties.

PMSM speed control based on intelligent sliding mode technique

PurposeThe purpose of this paper is to present a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor.Design/methodology/approachA novel recurrent radial basis function network (RBFN) is used to is used to approximate unknown nonlinear functions in permanent magnet synchronous motor (PMSM) dynamics. Then, using the functions obtained from the neural network, it is possible to design a model-based and precise controller for PMSM using the immersive modeling method.FindingsExperimental results indicate the appropriate performance of the proposed method.Originality/valueThis paper presents a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor. A novel recurrent RBFN is used to is used to approximate unknown nonlinear functions in PMSM dynamics.

Effect of topology changes of neural network in classification of weld defects

Non destructive testing play vital role in weld assessments. Time of Flight Diffraction testing is one of the reliable ultrasonic testing and evaluation method, followed globally. In this paper, TOFD testing based automatic weld classification is proposed. Artificial neural Network based algorithm is proposed and architecture, network topology of the feed forward network, Cascade forward network, Elman recurrent network and Radial basis function network are discussed. The classification of weld defects are also analyzed, based on their performance metric, classification accuracy. It also focuses on model development, neural network paradigms, training and testing of the proposed model for the time scale feature set.

Stream Flow Forecasting in Mahanadi River Basin using Artificial Neural Networks

Suspended sediment prediction is essential for wearing awayof soil and sedimentation at time of peakinundation period. Several kinds of neural networks have been projected in preceding papers for applications in hydrological events. This research intelligence focuses on assessment of applicability of Recurrent Neural Network (RNN) and Radial Basis Function Network (RBFN) for forecasting flow on daily basis at gauging station in Mahanadi river basin. Superlative performance is assessed on basis of RMSE, R2 which was attained by the precipitation and antecedent flow as model inputs. Based on performance value RNN gives prominent value as compare to RBFN. In case of RBFN three transfer function Tan-sig, Log-sig, purelin are utilized for evaluating model performance. While Tan-sig function used model gives extent value of R2 that is0.9614 and 0.9843 for training and testing phase respectively.

Temporally local recurrent radial basis function network for modeling and adaptive control of nonlinear systems

In this paper, a novel temporally local recurrent radial basis function network for modeling and adaptive control of nonlinear systems is proposed. The proposed structure consists of recurrent hidden neurons having weighted self-feedback loops and a weighted linear feed-through from the input layer directly to the output layer neuron(s). The dynamic back-propagation algorithm is developed and used for updating the parameters of the proposed structure. To improve the performance of learning algorithm, discrete Lyapunov stability method is used to develop an adaptive learning rate scheme. This scheme ensures the faster convergence of the parameters and maintains the stability of the system. A total of 5 complex nonlinear systems are used to test and compare the performance of the proposed network with other neural network structures. The disturbance rejection tests are also carried out to check whether the proposed scheme is able to handle the external disturbance/noise signals effects or not. The obtained results show the efficacy of the proposed method.

Nonlinear system identification based on a self-organizing type-2 fuzzy RBFN

This paper presents a new self-evolving recurrent Type-2 Fuzzy Radial Basis Function Network (T2FRBFN) in which the weights are considered Gaussian type-2 fuzzy sets and uncertain mean in each RBF neuron. The capability of the proposed T2FRBFN for function approximation and dynamical system identification perform better than the conventional RBFN. A novel type-2 fuzzy clustering is presented to add or remove the hidden RBF neurons. For parameter learning, back-propagation with adaptive learning rate is used. Finally the proposed T2FRBFN is applied to identification of three nonlinear systems as case studies. A comparison between T2FRBFN and the conventional RBFN as well as the method of Rubio-Solis and Panoutsos (2015) is presented. Simulation results and their statistical description show that the proposed T2FRBFN perform better than the conventional RBFN.

Adaptive hybrid control system using a recurrent RBFN-based self-evolving fuzzy-neural-network for PMSM servo drives

In this paper, an adaptive hybrid control system (AHCS) based on the computed torque control for permanent-magnet synchronous motor (PMSM) servo drive is proposed. The proposed AHCS incorporating an auxiliary controller based on the sliding-mode, a recurrent radial basis function network (RBFN)-based self-evolving fuzzy-neural-network (RRSEFNN) controller and a robust controller. The RRSEFNN combines the merits of the self-evolving fuzzy-neural-network, recurrent-neural-network and RBFN. Moreover, it performs the structure and parameter-learning concurrently. Furthermore, to relax the requirement of the lumped uncertainty, an adaptive RRSEFNN uncertainty estimator is used to adaptively estimate the non-linear uncertainties online, yielding a controller that tolerate a wider range of uncertainties. Additionally, a robust controller is proposed to confront the uncertainties including approximation error, optimal parameter vector and higher order term in Taylor series. The online adaptive control laws are derived based on the Lyapunov stability analysis, so that the stability of the AHCS can be guaranteed. A computer simulation and an experimental system are developed to validate the effectiveness of the proposed AHCS. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the AHCS grants robust performance and precise dynamic response regardless of load disturbances and PMSM uncertainties.

Adaptive Dynamic Sliding-Mode Control System Using Recurrent RBFN for High-Performance Induction Motor Servo Drive

In this paper, an adaptive dynamic sliding-mode control system (ADSMCS) with recurrent radial basis function network (RRBFN) for indirect field-orientation control induction motor (IM) drive is proposed. The ADSMCS comprises a dynamic sliding-mode controller (DSMC), an RRBFN uncertainty observer and a robust controller. The DSMC is proposed to reduce the chattering phenomenon. However, due to the uncertainty bound being unknown of the switching function for the DSMC, an ADSMCS is proposed to increase the robustness and improve the control performance of IM drive. In the ADSMCS, an RRBFN uncertainty observer is used to estimate an unknown nonlinear time-varying function of lumped parameter uncertainty online. Moreover, the adaptive learning algorithms for the RRBFN are derived using the Lyapunov stability theorem to train the parameters of the RRBFN online. Furthermore, a robust controller is proposed to confront the uncertainties including approximation error, optimal parameter vector and higher order term in Taylor series. A computer simulation and an experimental system are developed to validate the effectiveness of the proposed ADSMCS. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the ADSMCS grants robust performance and precise response regardless of load disturbances and IM uncertainties.

Improved Un-Gauged Streamflow Prediction Using Three Artificial Neural Networks Methods for Leeser Zab

Artificial neural networks have flexible structures that allow multi-input and multi-output modeling's. This is particularly important in streamflow forecasting where inflows at multiple locations are considered. Three different artificial neural networks methods which are feed forward neural networks FFNN, layered recurrent neural network LRNN and radial basis function networks RBFNN were investigated in this research to predict the un gauged Leeser Zab data north Iraq at two locations. Comparisons were made between the performance of the three methods to cope the missing data at two gauging stations for this stream. In order to accomplish this task , historical inflow series is employed for training, validation and test with different proportions, many structures of ANNs were used by selecting different combinations of inputs and using different number of neurons at the hidden layer. Also a different selections for the outputs by using single and multiple outputs were tried. The ANN prediction models has been found to be workable tool for prediction Leeser Zab stream flow correctly but the layer recurrent neural network kind and radial basis function neural networks were most suitable to reflect the behavior of the Leeser Zab flow especially the RBFNN type since the predicted flow results using this method were quite cohesive to the exact data flows. It was concluded from the experiment that lower proportion of training, validation and test ratio gives better result with higher accuracy

Neural network-based adaptive multiuser detection schemes in SDMA–OFDM system for wireless application

Neural network applications in adaptive multiuser detection (MUD) schemes are suggested here in the context of space division multiple access–orthogonal frequency division multiplexing system. In this paper, various neural network (NN) models like feed forward network (FFN), recurrent neural network (RNN) and radial basis function network (RBFN) are adopted for MUD. MUD using NN models outperforms other existing schemes like genetic algorithm--assisted minimum bit error rate (MBER) and minimum mean square error MUDs in terms of BER performance and convergence speed. Among these NN models, the FNN MUD performs efficiently as RNN in full load scenario, where the number of users is equal to number of receiving antennas. In overload scenario, where the number of users is more than the number of receiving antennas, the FNN MUD performs better than RNN MUD. Further, the RBFN MUD provides a significant enhancement in performance over FNN and RNN MUDs under both overload and full load scenarios because of its better classification ability due to Gaussian nonlinearity. Extensive simulation analysis considering Stanford University Interim channel models applied for fixed wireless applications shows improvement in convergence speed and BER performance of the proposed NN-based MUD algorithms.

A NEW RBF NEURAL NETWORK FOR PREDICTION IN INDUSTRIAL CONTROL

A novel neural architecture for prediction in industrial control: the 'Double Recurrent Radial Basis Function network' (R2RBF) is introduced for dynamic monitoring and prognosis of industrial processes. Three applications of the R2RBF network on the prediction values confirmed that the proposed architecture minimizes the prediction error. The proposed R2RBF is excited by the recurrence of the output looped neurons on the input layer which produces a dynamic memory on both the input and output layers. Given the learning complexity of neural networks with the use of the back-propagation training method, a simple architecture is proposed consisting of two simple Recurrent Radial Basis Function networks (RRBF). Each RRBF only has the input layer with looped neurons using the sigmoid activation function. The output of the first RRBF also presents an additional input for the second RRBF. An unsupervised learning algorithm is proposed to determine the parameters of the Radial Basis Function (RBF) nodes. The K-means unsupervised learning algorithm used for the hidden layer is enhanced by the initialization of these input parameters by the output parameters of the RCE algorithm.

Modeling piezoelectric actuators with Hysteretic Recurrent Neural Networks

This paper describes the application of Hysteretic Recurrent Neural Networks (HRNNs) to the modeling of polycrystalline piezoelectric actuators. Because piezoelectric materials exhibit voltage/strain relationships that are hysteretic and rate-dependent, the HRNN is composed of neurons with activation functions that incorporate these characteristics. Individual neurons are shown to agree with existing models of ideal single-crystal piezoelectric behavior. The combination of many such neurons into a network allows prediction of the heterogeneous behavior of polycrystalline materials. This model is shown to approximate the strain and polarization of an unloaded commercial stack actuator at multiple loading rates. A comparison is made to a recurrent Radial Basis Function Network model, and the HRNN is demonstrated to more accurately generalize across data sets. The model is further shown to execute on a PC platform at rates over 100 Hz, fast enough to support its application to real-time control.

WITHDRAWN: Recurrent NARX time series model prediction: A feedforward and recurrent radial basis function network

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.

Towards Accurate and Reproducible Predictions for Prognostic: an Approach Combining a RRBF Network and an AutoRegressive Model

In prognostic's field, the lack of knowledge on the behavior of equipments can impede the development of classical dependability analysis, or the building of effective physic-based models. Following that, artificial neural networks (ANNs) appear to be well suited since they can learn from data gathered from equipments. In this paper, an approach combining a Recurrent Radial Basis Function network (RRBF) and an AutoRegressive with eXogenous inputs model (ARX) is proposed in order to perform the prediction step of prognostics: the ARX attempts to correct the error of predictions of the RRBF. Moreover, since performances of an ANN can be closely related to initial parameterization of the network, a criterion is defined to quantify the reproducibility of predictions and thereby a priori estimate the usefulness of neural network structure. The whole aims at improving the prediction step of prognostics, which is critical with respects to real applicative conditions.

Réseau de neurones doublement récurrent à base de fonctions radiales : RR2FR Application au pronostic prévisionnel

This paper introduces a new neural architecture for prediction: Double Recurrent Radial Basis Function network (R 2 RBF). The proposed R 2 RFR is excited by the recurrence of output looped neurons on the input layer. An unsupervised learning algorithm is proposed to determine the parameters of the Radial Basis Function (RBF) nodes. An application of the R 2 RBF network on the values prediction of the time series confirmed that the proposed architecture minimizes the prediction error.

Radial Basis Function Nets for Time Series Prediction

This paper introduces a novel ensemble learning approach based on recurrent radial basis function networks (RRBFN) for time series prediction with the aim of increasing the prediction accuracy. Standing for the base learner in this ensemble, the adaptive recurrent network proposed is based on the nonlinear autoregressive with exogenous input model (NARX) and works according to a multi-step (MS) prediction regime. The ensemble learning technique combines various MSNARX-based RRBFNs which differ in the set of controlling parameters. The evaluation of the approach includes a discussion on the performance of the individual predictors and their combination

Neural Network Based Traffic Prediction for Wireless Data Networks

In a wireless network environment accurate and timely estimation or prediction of network traffic has gained much importance in the recent past. The network applications use traffic prediction results to maintain its performance by adopting its behaviors. Network Service provider will use the prediction values in ensuring the better Quality of Service(QoS) to the network users by admission control and load balancing by inter or intra network handovers. This paper presents modeling and prediction of wireless network traffic. Here traffic is modeled as nonlinear and non-stationary time series. The nonlinear and non-stationary time series traffic is predicted using neural network and statistical methods. The results of both the methods are compared on different time scales or time granularity. The Neural Network (NN) architectures used in this study are Recurrent Radial Basis Function Network (RRBFN) and Echo state network (ESN).The statistical model used here in this work is Fractional Auto Regressive Integrated Moving Average (FARIMA) model. The traffic prediction accuracy of neural network and statistical models are in the range of 96.4% to 98.3% and 78.5% to 80.2% respectively.

A Time Series Modeling and Prediction of Wireless Network Traffic

The number of users and their network utilization will enumerate the traffic of the network. The accurate and timely estimation of network traffic is increasingly becoming important in achieving guaranteed Quality of Service (QoS) in a wireless network. The better QoS can be maintained in the network by admission control, inter or intra network handovers by knowing the network traffic in advance. Here wireless network traffic is modeled as a nonlinear and nonstationary time series. In this framework, network traffic is predicted using neural network and statistical methods. The results of both the methods are compared on different time scales or time granularity. The Neural Network(NN) architectures used in this study are Recurrent Radial Basis Function Network (RRBFN) and Echo state network (ESN).The statistical model used here in this work is Fractional Auto Regressive Integrated Moving Average (FARIMA) model. The traffic prediction accuracy of neural network and statistical models are in the range of 96.4% to 98.3% and 78.5% to 80.2% respectively.

An adaptive recurrent radial basis function network tracking controller for a two-dimensional piezo-positioning stage

An adaptive recurrent radial basis function network (ARRBFN) tracking controller for a two-dimensional piezo-positioning stage is proposed in this study. First, a mathematical model that represents the dynamics of the two-dimensional piezo-positioning stage is proposed. In this model, a hysteresis friction force that describes the hysteresis behavior of one-dimensional motion is used; and a nonconstant stiffness with the cross-coupling dynamic due to the effect of bending of a lever mechanism in x and y axes also is included. Then, according to the proposed mathematical model, an ARRBFN tracking controller is proposed. In the proposed ARRBFN control system, a recurrent radial basis function network (RRBFN) with accurate approximation capability is used to approximate an unknown dynamic function. The adaptive learning algorithms that can learn the parameters of the RRBFN on line are derived using Lyapunov stability theorem. Moreover, a robust compensator is proposed to confront the uncertainties, including approximation error, optimal parameter vectors, higher-order terms in Taylor series. To relax the requirement of the value of the lumped uncertainty in the robust compensator, an adaptive law is investigated to estimate the lumped uncertainty. Using the proposed control scheme, the position tracking performance is substantially improved and the robustness to uncertainties, including hysteresis friction force and cross-coupling stiffness, can be obtained as well. The tracking performance and the robustness to external load of the proposed ARRBFN control system are illustrated by some experimental results.

Recurrent RBFN-based fuzzy neural network control for X-Y-/spl Theta/ motion control stage using linear ultrasonic motors

A recurrent radial basis function network (RBFN) based fuzzy neural network (FNN) control system is proposed to control the position of an X-Y-theta motion control stage using linear ultrasonic motors (LUSMs) to track various contours in this study. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, the structure and the parameter learning phases of the recurrent RBFN-based FNN are performed concurrently and on line. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient decent method using a delta adaptation law. The experimental results due to various contours show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties.