Boundedness Of Parameters Research Articles

A method of multiple model adaptive control of affine systems and its application to aero-engines

This paper investigates the multiple model adaptive control problem of affine systems with unknown parameters. Firstly, an adaptive controller with resettable parameters and an adaptive law with projection function are designed to ensure the asymptotic tracking for the reference system and the boundedness of parameters. Secondly, a transformation of system is given to enable a finite-time parameter estimator to calculate the uncertain parameters in the system matrix and the affine item simultaneously. Then, a novel performance index to describe the error between the controlled plant and the identification model is given to orchestrate switchings among identification models aiming to choose the best one. Next, the sufficient condition of the asymptotic convergence for the system error is given. Finally, all designs are evaluated in a hardware-in-the-loop simulation platform of an aero-engine control system and compared with three other methods, the effectiveness and superiority are verified.

Moral Violation With and Without Physical Impurity Modulates Fairness Consideration: Evidence From EEG and ERP

The stable neural networks are the models where their variables and parameters remain bounded through the time and where the overfitting is avoided. A model with overfit has many parameters relative to the number of data, and it has poor predictive performance because it overreacts to minor fluctuations in the data. This paper presents a method to obtain a stable algorithm for the learning of a radial basis function neural network. The method consists of: 1) the radial basis function neural network is linearized, 2) the algorithm for the learning of the radial basis function neural network is introduced, 3) stability of the mentioned technique is assured, 4) convergence of the suggested method is guaranteed, and 5) boundedness of parameters in the focused technique is assured. The above mentioned method is applied for the learning of two mechatronic processes.

Neural Network Identification and Direct Adaptive Fuzzy Neural Network (DAFNN) Controller for Unknown Nonlinear Non-affine Pneumatic Servo System

In this paper, a direct adaptive fuzzy neural network (DAFNN) controller for trajectory tracking control of the non-linear non-affine pneumatic servo system is presented. First, using a neural network identifier, the non-linear dynamics of a real pneumatic servo system is simulated. By comparing the output of the neural network and the output of the experimental setup, it is observed that the non-linear pneumatic actuator system is well-identified using neural networks. By incorporating the Lyapunov stability theorem, the adaptive laws for the parameters of the controller are obtained, parameter boundedness and stability of the closed-loop system are guaranteed. Finally, practical results are successfully implemented for trajectory tracking control of the pneumatic servo system, in which the effect of the simultaneous updating of the antecedent and consequent’s parameters of the fuzzy neural network controller has been investigated. The tracking error ±1.3mm and ±1 mm for proposed updating method compared to ±2.5mm and ±3.5mm, for a case that the weigh parameters are merely adjusted, are obtained. The results indicate the proposed adjustment method improves the performance of the controller in the presence of unknown nonlinearities and dynamics uncertainty.

Distributed Online Optimization for Multi-Agent Networks With Coupled Inequality Constraints

This article investigates the distributed online optimization problem over a multi-agent network subject to local set constraints and coupled inequality constraints, which has a lot of applications in many areas, such as wireless sensor networks, power systems, and plug-in electric vehicles. In this problem, the cost function at each time step is the sum of local cost functions with each of them being gradually revealed to its corresponding agent, and meanwhile only local functions in coupled inequality constraints are accessible to each agent. To address this problem, a modified primal-dual algorithm, called distributed online primal-dual push-sum algorithm, is developed in this article, which does not rest on any assumption on parameter boundedness and is applicable to unbalanced networks. It is shown that the proposed algorithm is sublinear for both the dynamic regret and the violation of coupled inequality constraints. Finally, the theoretical results are supported by a simulation example.

Designing and Implementation of Stable Sinusoidal Rough-Neural Identifier.

A rough neuron is defined as a pair of conventional neurons that are called the upper and lower bound neurons. In this paper, the sinusoidal rough-neural networks (SR-NNs) are used to identify the discrete dynamic nonlinear systems (DDNSs) with or without noise in series-parallel configuration. In the identification of periodic nonlinear systems, sinusoidal activation functions provide more efficient neural networks than the sigmoidal activation functions. Based on the Lyapunov stability theory, an online learning algorithm is developed to train the SR-NNs. The asymptotically convergence of the identification error to zero and the boundedness of parameters as well as predictions are proved. SR-NNs are used to identify some DDNSs and the cement rotary kiln (CRK). CRK is a complex nonlinear system in the cement factory, which produces the cement clinker. The experiments show that the SR-NNs in the identification of nonlinear systems have better performances than multilayer perceptrons (MLPs), sinusoidal neural networks, and rough MLPs, particularly in the presence of noise.

Uniform stable radial basis function neural network for the prediction in two mechatronic processes

The stable neural networks are the models where their variables and parameters remain bounded through the time and where the overfitting is avoided. A model with overfit has many parameters relative to the number of data, and it has poor predictive performance because it overreacts to minor fluctuations in the data. This paper presents a method to obtain a stable algorithm for the learning of a radial basis function neural network. The method consists of: 1) the radial basis function neural network is linearized, 2) the algorithm for the learning of the radial basis function neural network is introduced, 3) stability of the mentioned technique is assured, 4) convergence of the suggested method is guaranteed, and 5) boundedness of parameters in the focused technique is assured. The above mentioned method is applied for the learning of two mechatronic processes.

Least square neural network model of the crude oil blending process

In this paper, the recursive least square algorithm is designed for the big data learning of a feedforward neural network. The proposed method as the combination of the recursive least square and feedforward neural network obtains four advantages over the alone algorithms: it requires less number of regressors, it is fast, it has the learning ability, and it is more compact. Stability, convergence, boundedness of parameters, and local minimum avoidance of the proposed technique are guaranteed. The introduced strategy is applied for the modeling of the crude oil blending process.

Semántica léxica y estructuración aspectual/eventiva: telicidad, perfectividad y delimitación en el análisis de las cópulas españolas

La alternancia ser/estar ha inspirado numerosos estudios y, consecuentemente, una significativa variedad de conceptos técnicos. Ante este panorama, proponemos avanzar hacia un refinamiento de los parámetros aspectuales empleados, con énfasis en los niveles lingüísticos en los que se originan y en la diferenciación entre semántica léxica/construccional. A partir de diagnósticos específicos, proponemos: primero, que ser y estar se alejan de la noción estándar de cópula y verbo estativo y son cuanto menos ambiguas respecto al tipo eventivo de las construcciones que articulan; segundo, que propiedades asignadas en la literatura no responden a lacópula, sino a la construcción; tercero, que la delimitación [boundedness] capta más eficientemente las situación de ser/estar, como propiedad semántica diferente y lógicamente distinguible de la perfectividad y telicidad.