Weights Of Radial Basis Function Network Research Articles

Learning from output‐feedback control of sampled‐data systems in normal form

AbstractThis paper investigates the learning and control problem of sampled‐data systems with only output measurements. A unified approach is presented by integrating the sampled‐data observer and deterministic learning. First, an adaptive radial basis function network (RBFN) learning controller with a sampled‐data observer is designed to track a recurrent reference model. Along the trajectory estimated by the observer, it is proven that the RBFN weights can exponentially converge to their ideal values with the satisfaction of a persistent excitation (PE) condition and the closed‐loop dynamics can be accurately learned during the output‐feedback process. Second, by using the learning results, a knowledge‐based output‐feedback controller is developed to improve the tracking performance. Further research shows that choosing appropriate parameters for the observer and RBFN can guarantee learning and control performance. The significance of the proposed approach is that the closed‐loop dynamics of the output‐feedback process can be accurately learned and further utilized to improve control performance. Simulation studies indicate the effectiveness and advantages of the learning control approach.

Adaptive composite anti-disturbance control for heavy haul trains

Abstract In this paper, an adaptive composite anti-disturbance control of heavy haul trains (HHTs) is proposed. First, the mechanical principle and characteristics of couplers are analysed and the longitudinal multi-particles nonlinear dynamic model of HHTs is established, which can satisfy that the forces of vehicles in different positions are different. Subsequently, a radial basis function network (RBFNN) is employed to approximate the uncertainties of HHTs, and a nonlinear disturbance observer (NDO) is constructed to estimate the approximation error and external disturbances. To indicate and improve the approximation accuracy, a serial-parallel identification model of HHTs is constructed to generate a prediction error, and an adaptive composite anti-disturbance control scheme is developed, where the prediction error and tracking error are employed to update RBFNN weights and an auxiliary variable of NDO. Finally, the feasibility and effectiveness of the proposed control scheme are demonstrated through the Lyapunov theory and simulation experiments.

Distribution consensus of nonlinear stochastic multi-agent systems based on sliding-mode control with probability density function compensation

Strict consensus is difficult to be implemented due to the stochastic behavior of multi-agent systems (MASs), so a new concept, distribution consensus, is proposed here to keep the agents’ consensus in the stochastic sense, i.e., the output errors do not converge to a fixed value but follow a desired distribution function. The appropriate control protocol, with the output error probability density function (PDF) as the target, is designed based on the combination of sliding mode control and PDF compensation. Sliding mode control is the core part to ensure the whole system’s stability, and the PDF compensator is used to compensate the random variation and reduce the chattering effect, respectively. In order to realize the complete control in real time, the PDF compensator is modeling by a radial basis function (RBF) neural network and its optimal control law is calculated by the iterative training of RBF network weights. Finally, the effectiveness of the proposed method is verified by MASs simulations with three different communication topologies. The PDF compensator can greatly improve the consensus effect for the nonlinear stochastic MASs.

Evaluation of Financial Performance of Port Enterprises Based on Radial Basis Function Neural Network

Li, J., 2020. Evaluation of financial performance of port enterprises based on radial basis function neural network. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 255–258. Coconut Creek (Florida), ISSN 0749-0208.An objective, fair, and accurate evaluation of the financial performance of the port enterprises is an important measure to mobilize and improve the enthusiasm of the port enterprises employees and the progress of scientific and technological innovation. This paper proposes a refined evaluation model of the financial performance of port enterprise companies based on the radial basis function (RBF) neural network. The normalized financial performance index data of port enterprise companies are multiplied by the corresponding weight coefficient as the network input, using excellent, good, middle, and F5 grade evaluations as output. The particle swarm optimization algorithm was used to cross-validate the RBF network structure parameters. Through analyzing the RBF network structure and enter and export characteristics, it was found that the trained RBF network weights are highly correlated with the five-level evaluation results and can more accurately distinguish scientific research performance differences than the five-level evaluation results. This weight can be directly used to conduct a detailed evaluation of the financial performance of the port enterprises. This paper promotes the application of RBF networks in the evaluation of financial performance of port enterprise companies and provides new ideas for similar evaluations.

A New Compensation Method for Sensor-Drift Effect Based on the Cross-Correntropy Concept

Cross-correntropy which has the characteristics of correlation and entropy can be a generalized similarity measure of two probability density functions (PDFs). In this paper, we apply the cross-correntropy concept to sensor-signal processing for sensor drift compensation by maximizing the correntropy between the Parzen PDF of test-phase system output and the target pattern. The gas sensing system uses a conducting polymer sensor array and signal processing unit equipped with radial basis function network (RBFN). The RBFN weights are readjusted in the test phase by the proposed method for sensor drift compensation

A method of independent component analysis based on radial basis function networks using noise estimation

AbstractThis paper proposes a robust independent component analysis (ICA) approach for noise reduction. Noise reduction is a difficult problem in ICA model. In general signal processing applications, there is more than one interference signal which may have unknown characteristics. In these situations, traditional linear ICA may lead to poor results. Hence, noise reduction is preferred to be performed with nonlinear adaptive filtering. In this paper, a radial basis function network (RBFN) is employed to transform the observed signals into output space in a nonlinear manner. The weights of RBFN are updated by utilizing a modified fixed‐point algorithm. The proposed method has not only the capacity of recovering the mixed signals, but also reducing noise with unknown characteristics from observed signals. The simulation results and analysis show that the proposed algorithm is suitable for practical unsupervised noise reduction problem. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(3): 45–52, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10073

A Method of Independent Component Analysis Based on Radial Basis Function Networks Using Noise Estimation

This paper proposes a robust independent component analysis (ICA) approach for noise reduction. Noise reduction is a difficult problem in ICA model. In general signal processing applications, there is more than one interference signal which may have unknown characteristics. In these situations, traditional linear ICA may lead to poor results. Hence, noise reduction is preferred to be performed with nonlinear adaptive filtering. In this paper, a radial basis function network (RBFN) is employed to transform the observed signals into output space in a nonlinear manner. The weights of RBFN are updated by utilizing a modified fixed-point algorithm. The proposed method has not only the capacity of recovering the mixed signals, but also reducing noise with unknown characteristics from observed signals. The simulation results and analysis show that the proposed algorithm is suitable for practical unsupervised noise reduction problem. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(3): 45–52, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10073

Kernel orthonormalization in radial basis function neural networks

This paper deals with optimization of the computations involved in training radial basis function (RBF) neural networks. The main contribution of the reported work is the method for network weights calculation, in which the key idea is to transform the RBF kernels into an orthonormal set of functions (using the standard Gram-Schmidt orthogonalization). This significantly reduces the computing time if the RBF training scheme, which relies on adding one kernel hidden node at a time to improve network performance, is adopted. Another property of the method is that, after the RBF network weights are computed, the original network structure can be restored back. An additional strength of the method is the possibility to decompose the proposed computing task into a number of parallel subtasks so gaining further savings on computing time. Also, the proposed weight calculation technique has low storage requirements. These features make the method very attractive for hardware implementation. The paper presents a detailed derivation of the proposed network weights calculation procedure and demonstrates its validity for RBF network training on a number of data classification and function approximation problems.