Variable Structure Radial Basis Function Research Articles

A Real-Time Sequential Ship Roll Prediction Scheme Based on Adaptive Sliding Data Window

A ship roll prediction scheme is proposed using an adaptive sliding data window (SDW), which is designed to represent time-varying nonlinear dynamics of ship roll motion. The adjustment of SDW is realized by developing an improved fuzzy Gath-Geva (IFGG) segmentation approach, which detects the changes of system dynamics and thereby automatically adapting the scale of SDW. By virtue of the learning scheme with an adaptive SDW, the variable-structure radial basis function network is constructed sequentially to online predict ship roll dynamics. Experimental studies on online ship roll prediction are conducted on measured data from YuKun ’s full-scale sea trial. Results demonstrate the remarkable predictive accuracy of the proposed ship roll prediction model as well as the effectiveness of the IFGG-based SDW in terms of representing time-varying dynamics.

Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate

The neural network has the advantages of self-learning, self-adaptation, and fault tolerance. It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns. However, the structure and the convergence rate of the radial basis function (RBF) neural network need to be improved. This paper proposes a new variable structure radial basis function (VS-RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network. The number of neurons in the hidden layer is adjusted by calculating the output information of neurons in the hidden layer and the multi-information between neurons in the hidden layer and output layer. This method effectively solves the problem that the RBF neural network structure is too large or too small. The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm. At the same time, the convergence analysis of the VS-RBF neural network is given to ensure the stability of the RBF neural network. Compared with other self-organizing RBF neural networks (self-organizing RBF (SORBF) and rough RBF neural networks (RS-RBF)), VS-RBF has a more compact structure, faster dynamic response speed, and better generalization ability. The simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS-RBF.

A real-time ship roll motion prediction using wavelet transform and variable RBF network

Real-time prediction of ship roll motion is vital for marine safety and efficiency of operations onboard the ship. However, ship roll motion is a complex time-varying nonlinear process which varies with various sailing conditions as well as time-varying environmental factors. To achieve precise real-time ship roll prediction, an ensemble prediction scheme is constructed by combining the discrete wavelet transform (DWT) method with the variable-structure radial basis function (RBF) network. The DWT is used to reduce the time-series data redundancies and carry the data information in few significant uncoupled sub-series, thus facilitate the identification and prediction by using the variable RBF networks. The variable RBF networks are used to represent time-varying dynamics with both the structure and parameters are tuned in real time. The DWT-transform-based variable RBF networks are used to represent the time-varying nonlinear dynamics of ship roll movement during ship maneuvering. The effectiveness of the proposed DWT-based real-time roll prediction scheme is demonstrated by short-term ship roll motion prediction experiments based on the actual ship roll motion measurements collected during sea test of M.V. YuKun.

Variable neural adaptive robust control: a switched system approach.

Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multiinput multioutput uncertain systems. The controllers incorporate a novel variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. It can determine the network structure online dynamically by adding or removing RBFs according to the tracking performance. The structure variation is systematically considered in the stability analysis of the closed-loop system using a switched system approach with the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.

ONLINE GREY PREDICTION OF SHIP ROLL MOTION USING VARIABLE RBFN

Ship's roll motion at sea is a complex system featured by nonlinearity, uncertainty, and time-varying dynamics. In this paper, a grey neural prediction scheme is presented for online ship roll motion prediction. The grey data processing approach is employed to alleviate the unfavorable effects of the uncertainty exhibited in measurement data, and grey relational analysis method is also involved to determine the structure of the grey prediction scheme. To represent the time-varying dynamics of nonlinear system, a variable structure radial basis function network (RBFN) is online constructed by learning samples in a sliding data window sequentially. Simulations of ship roll motion prediction are conducted via different approaches to validate the effectiveness of the proposed variable-RBFN-based grey prediction method. Measurement data employed in simulation is obtained during sea trials of the scientific research and training ship Yu Kun. Simulation results demonstrate the efficiency and accuracy of the proposed prediction method.

On-line prediction of ship roll motion during maneuvering using sequential learning RBF neuralnetworks

The on-line prediction of ship roll motion during maneuvering plays an important role in navigation safety and ship control applications. This paper presents an on-line prediction model of ship roll motion via a variable structure radial basis function neural network (RBFNN), whose structure and parameters are tuned in real time based on a sliding data window observer. The RBFNN is sequentially constructed by adding the new sample in the hidden layer and pruning the obsolete hidden units at each epoch, with the connecting parameters adjusted simultaneously. Gaussian functions with multi-scale kernel width are adopted to provide more flexible representations of model input terms and to achieve better generalization capability. Simulation study of ship roll motion prediction is conducted with measurement data of turning circle test and zigzag test in full-scale sea trial. Results demonstrate that the proposed neural network predictive model can on-line predict the roll angle with high accuracy. The predictive model is also featured with its compact network structure and fast computationalspeed.

Variable Neural Direct Adaptive Robust Control of Uncertain Systems

Direct adaptive robust state and output feedback controllers are proposed for the output tracking control of a class of uncertain systems. The proposed controllers incorporate a variable structure radial basis function (RBF) network to approximate unknown system dynamics, where the RBF network can determine its structure online dynamically. Radial basis functions can be added or removed to ensure the desired tracking accuracy and to prevent the network redundancy simultaneously. The closed-loop systems driven by the direct adaptive robust controllers are characterized by the guaranteed transient and steady-state tracking performance. The performance of the proposed output feedback controller is illustrated with numerical simulations.