Generalized Hybrid Constructive Learning Algorithm for Multioutput RBF Networks

Adaptive Multioutput Gradient RBF Tracker for Nonlinear and Nonstationary Regression

Multioutput regression of nonlinear and nonstationary data is largely understudied in both machine learning and control communities. This article develops an adaptive multioutput gradient radial basis function (MGRBF) tracker for online modeling of multioutput nonlinear and nonstationary processes. Specifically, a compact MGRBF network is first constructed with a new two-step training procedure to produce excellent predictive capacity. To improve its tracking ability in fast time-varying scenarios, an adaptive MGRBF (AMGRBF) tracker is proposed, which updates the MGRBF network structure online by replacing the worst performing node with a new node that automatically encodes the newly emerging system state and acts as a perfect local multioutput predictor for the current system state. Extensive experimental results confirm that the proposed AMGRBF tracker significantly outperforms existing state-of-the-art online multioutput regression methods as well as deep-learning-based models, in terms of adaptive modeling accuracy and online computational complexity.

Variable Structure Control of a Small Ducted Wind Turbine in the Whole Wind Speed Range Using a Luenberger Observer

This paper proposes a new variable structure control scheme for a variable-speed, fixed-pitch ducted wind turbine, equipped with an annular, brushless permanent-magnet synchronous generator, considering a back-to-back power converter topology. The purpose of this control scheme is to maximise the aerodynamic power over the entire wind speed range, considering the mechanical safety limits of the ducted wind turbine. The ideal power characteristics are achieved with the design of control laws aimed at performing the maximum power point tracking control in the low wind speeds region, and the constant speed, power, and torque control in the high wind speed region. The designed control laws utilize a Luenberger observer for the estimation of the aerodynamic torque and a shallow neural network for wind speed estimation. The effectiveness of the proposed method was verified through tests in a laboratory setup. Moreover, a comparison with other solutions from the literature allowed us to better evaluate the performances achieved and to highlight the originality of the proposed control scheme.

Learning Optimized Structure of Neural Networks by Hidden Node Pruning With L1 Regularization

We propose three different methods to determine the optimal number of hidden nodes based on L₁ regularization for a multilayer perceptron network. The first two methods, respectively, use a set of multiplier functions and multipliers for the hidden-layer nodes and implement the L₁ regularization on those, while the third method equipped with the same multipliers uses a smoothing approximation of the L₁ regularization. Each of these methods begins with a given number of hidden nodes, then the network is trained to obtain an optimal architecture discarding redundant hidden nodes using the multiplier functions or multipliers. A simple and generic method, namely, the matrix-based convergence proving method (MCPM), is introduced to prove the weak and strong convergence of the presented smoothing algorithms. The performance of the three pruning methods has been tested on 11 different classification datasets. The results demonstrate the efficient pruning abilities and competitive generalization by the proposed methods. The theoretical results are also validated by the results.

Coordinated Optimization for the Descent Gradient of Technical Index in the Iron Removal Process

In the iron removal process, which is composed of four cascaded reactors, outlet ferrous ion concentration (OFIC) is an important technical index for each reactor. The descent gradient of OFIC indicates the reduced degree of ferrous ions in each reactor. Finding the optimal descent gradient of OFIC is tightly close to the effective iron removal and the optimal operation of the process. This paper proposes a coordinated optimization strategy for setting the descent gradient of OFIC. First, an optimal setting module is established to determine the initial set-points of the descent gradient. The oxygen utilization ratio (OUR), an important parameter in this module, cannot be measured online. Therefore, a self-adjusting RBF (SARBF) neural network with an adaptive learning rate is developed to estimate the OUR. The convergence of the SARBF neural network is discussed. Then, a coordinated optimization strategy is proposed to adjust the set-points of the descent gradient when the measured OFICs drift away from their desired set-pints. If the final OFIC does not satisfy the process requirements, a compensation mechanism is developed to provide a compensation for the set-points of the descent gradient. Finally, industrial experiments in the largest zinc hydrometallurgy plant validate the effectiveness of the proposed coordinated optimization strategy. Our strategy improves the qualified ratio of the OFIC and the quality of the goethite precipitate. More profit is created to the iron removal process after our strategy is applied.