Implementation of dynamic matrix control on field programmable gate array

High performance computer is often required by model predictive control (MPC) systems due to the heavy online computation burden. To extend MPC to more application cases with low-cost computation facilities, the implementation of MPC controller on field programmable gate array (FPGA) system is studied. For the dynamic matrix control (DMC) algorithm, the main design idea and the implemental strategy of DMC controller are introduced based on a FPGA’s embedded system. The performance tests show that both the computation efficiency and the accuracy of the proposed controller can be satisfied due to the parallel computing capability of FPGA.     

Soft-Sensing Method with Online Correction Based on Semi-Supervised Learning

Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In this work, a semi-supervised learning(SSL) method is proposed to build the soft-sensing model by use of the unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The mechanism estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.     

DYNAMIC RESOURCE CONTROL OF THE VIRTUAL PATH IN ATM NETWORKS

ＩｎｔｒｏｄｕｃｔｉｏｎＡｓｙｎｃｈｒｏｎｏｕｓＴｒａｎｓｆｅｒＭｏｄｅｈａｓｂｅｅｎｇｉｖｅｎｃｏｎｓｉｄｅｒａｂｌｅａｔｔｅｎｔｉｏｎａｓａｐｏｗｅｒｗａｙｏｆａｃｃｏｍ－ｐｌｉｓｈｉｎｇａｎｉｎｔｅｇｒａｔｅｄｓｅｒｖｉｃｅｎｅｔｗｏｒｋ［１］．ＩｎＡＴＭＮｅｔｗｏｒｋｓ，ｉｎｆｏｒｍａｔｉｏｎｉｓｄｉｖｉｄｅｄｉｎｔｏｓｈｏｒｔｆｉｘｅｄ－ｌｅｎｇｔｈｅｎｔｉｔｉｅｓ，ｃａｌｌｅｄｃｅｌｌｓ，ｗｈｉｃｈａｒｅｐｒｏｖｉｄｅｄｗｉｔｈｆｌｏｗｉｄｅｎｔｉｆｉｃａｔｉｏｎｌａｂｅｌｓ，ａｎｄ…     

A Short-Term Traffic Flow Forecasting Method and Its Applications

Short-term forecast of urban traffic flow is very important to intelligent transportation. Although the conventional methods have some advantages, to some extent, in improving the traffic forecast’s precision, it is still hard to achieve high accuracy. In this paper, we propose a short-term traffic flow forecasting method, which is based on the hybrid particle swarm optimization-neural network(HPSO-NN) with error compensation mechanism.In HPSO-NN, the hybrid PSO algorithm is employed to train the structures and parameters of the feed-forward advanced neural network, while the error compensation mechanism is employed to improve the accuracy. HPSONN is used to forecast the vehicle velocity in Shanghai North-South Viaduct. Experimental results show that the HPSO-NN, compared with the auto-regressive and moving average(ARMA) model, can forecast traffic flow with a higher accuracy. What’s more, we have also found that HPSO-NN with error compensation mechanism has better performance than that of HPSO-NN alone.     

BLIND DIFFERENTIAL ADAPTIVE MULTIUSER DETECTION

IntroductionThe problem of combating multi- access interfer-ence ( MAI) in CDMA systems has receivedgreat attention recently[1～ 3] . In Ref. [4 ] ,M.Honig employed a blind adaptive multiuser de-tector based on the minimization of mean- out-put- energy( MOE) to overcome MAI,and itre-quired no more knowledge than did the conven-tional single- user receiver.Surplus energy wasemployed to suppress the effects of multi- accessinterference and thus provided an optimal detec-tion solution. The…