By introducing a deadwzone scheme, a new neural network based adaptive iterative learning control （ILC） （NN-AILC） scheme is presented for nonlinear discrete-time systems, where the NN weights are time-varying. The...
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By introducing a deadwzone scheme, a new neural network based adaptive iterative learning control （ILC） （NN-AILC） scheme is presented for nonlinear discrete-time systems, where the NN weights are time-varying. The most distinct contribution of the proposed NN-AILC is the relaxation of the identical conditions of initial state and reference trajectory, which are common requirements in traditional ILC problems. Convergence analysis indicates that the tracking error converges to a bounded ball, whose size is determined by the dead-zone nonlinearity. Computer simulations verify the theoretical results.
The least squares support vector regression (LS-SVR) is usually used for the modeling of single output system, but it is not well suitable for the actual multi-input-multi-output system. The paper aims at the modeling...
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The least squares support vector regression (LS-SVR) is usually used for the modeling of single output system, but it is not well suitable for the actual multi-input-multi-output system. The paper aims at the modeling of multi-output systems by LS-SVR. The multi-output LS-SVR is derived in detail. To avoid the inversion of large matrix, the recursive algorithm of the parameters is given, which makes the online algorithm of LS-SVR practical. Since the computing time increases with the number of training samples, the sparseness is studied based on the pro-jection of online LS-SVR. The residual of projection less than a threshold is omitted, so that a lot of samples are kept out of the training set and the sparseness is obtained. The standard LS-SVR, nonsparse online LS-SVR and sparse online LS-SVR with different threshold are used for modeling the isomerization of C8 aromatics. The root-mean-square-error (RMSE), number of support vectors and running time of three algorithms are compared and the result indicates that the performance of sparse online LS-SVR is more favorable.
This paper presents a stochastic optimal controller using the våiable-period sampling Networked controlsystems (NCSs) model, where it has been assumed that the random network-induced delay only exists between se...
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Surface electromyogram (EMG) from elbow, wrist and hand has been widely used as an input of multifunction prostheses for many years. However, for patients with high-level limb deficiencies, muscle activities in upper-...
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An improved unsupervised neural network of ART2 is proposed to judge the pattern of blast furnace states. In this method six variables viz. charging speed, air flow, air temperature, air pressure, permeability indices...
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This paper contributes to the question of how to choose estimator memory length in system identification. Traditional approaches to this problem have been based on stationary models for parameter time variations. This...
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Nonlinear process identification is studied. In model identification, a linear parameter varying (LPV) model is used and it consists of weighted local linear models. In this work, predetermined weighting functions are...
Human sleep stages during whole night are usually classified into six stages based on polysommnographic (PSG) record. Sleep state of human light sleep changes gradually and continuously. In this study, automatic judgm...
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Considering different single and multiphase circuits feeding linear and non-linear loads, this paper presents theoretical discussions and experimental evaluation of the recent Conservative Power Theory (CPT), by means...
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Considering different single and multiphase circuits feeding linear and non-linear loads, this paper presents theoretical discussions and experimental evaluation of the recent Conservative Power Theory (CPT), by means of Virtual Instrumentation concepts. The main goal is to analyze the results of such power theory definitions under nonsinusoidal and unbalanced conditions, pointing out its major advantages, possible drawbacks or relevant aspects for discussion.
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