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Rapid, accurate and reliable measurements of biological oxygen demand (BOD) are a key basis for monitoring and controlling wastewater treatment processes (WWTP). A kind of soft measurement based on the dynamic neural network (DNN) is proposed in this paper, which can be used to monitor and model the important parameters of the wastewater treatment process on-line. The main parts of the soft measurement...
Nowadays congestion control problem of the intermediate nodes in the Internet has received extensively attention in networking and control community. In this paper, a novel adaptive PID (Proportional-Integral-Differential) controller based on neural networks for the problem of AQM with ECN marks is presented. Considering a previously developed nonlinear dynamic model of TCP/AQM system and the queue...
This paper is concerned with H∞ control problems for a class of uncertain nonlinear systems. In the procedure, neural networks (NNs) are used to model the nonlinear functions, H∞ tracking controller is derived based on Lyapunov function and the notion of dissipativeness. The controller can not only guarantee the stability of the overall control system, but also attenuate the effect of both the external...
This paper investigates the failure prediction problem for multivariable and multi-failure-mode complex systems based on performance degradation. In our treatment, neural network is employed to simulate system performance degradation and to predict the health states of functional modules. BP network and Learning Vector Quantization (LVQ) network are used simultaneously to simulate and to predict future...
The detection of abnormality in a facility is vital for plant operation. The methods by cameras and sensors are traditionally used but not sufficient for abnormal detection in the early stage. On the other hand, human feels the change of surroundings by various sensings such as eyes, noses, and ears. The diagnosis by the sound has the advantage of being able to detect the wide-ranging abnormalities...
Delay dependent decentralized robust H∞ control for a class of interconnected large-scale systems is considered. The time-delays are assumed as time-varying, and exist in interconnected and measurement output matrices. The decentralized output feedback H∞ controllers are designed. Combining the Lyapunov-Krasovskii functional approach and the delay integral inequality of matrices, the delay dependent...
A nonlinear multivariable adaptive decoupling PID control strategy based on multiple models and neural network is proposed for a class of uncertain discrete time nonlinear dynamical systems. The adaptive decoupling PID controller is composed of a linear adaptive PID decoupling controller, a neural network nonlinear adaptive PID decoupling controller and a switch mechanism. The PID parameters of such...
This paper presents Model predictive control (MPC) of nonlinear hybrid system based on neural network (NN) optimization. Multiple model method is used to modeling of nonlinear hybrid system and these models are combined using Bayes theorem. NN optimization combined gradient NN with recurrent NN is proposed to solve optimization problem of each sample time in MPC. An example of benchmark three spherical...
The connection-coefficients control is a new control model, which is proposed for regulating connection coefficients of the state variables the subsystems of the interconnected systems. It is the direct regulations for the connections of system states and is different from the traditional feedback compensation. This paper is focused on the Connection-Coefficients Synergic Stabilization Problem of...
In this paper, dropped the assumption of the boundedness of the activation functions, the global dynamics are investigated for the recurrently connected neural networks (RCNNs) with discontinuous activations and time-varying delays. Based on the nonsmooth analysis theory, linear matrix inequality (LMI) technique and differential inclusions approach, several sufficient conditions are obtained to ensure...
In this paper, adaptive neural network (NN) control is investigated for a class of block triangular multi-inputmulti-output (MIMO) nonlinear discrete-time systems with each subsystem in pure-feedback form with unknown control directions. Each subsystem is transformed into a predictor form such that the noncausal problem can be avoided in the control design. By exploring the properties of block triangular...
This paper presents the design of a neural network-based feedback linearisation (NARMA-L2) slip controller for an anti-lock braking system (ABS). The dynamics of the electro-mechanical based braking system are incorporated in the ABS model and thus a slip controller is developed to minimise the braking distance. The proposed controller is compared with an optimally-tuned PID controller. Simulation...
In order to deal with the control problems of nonlinearity and difficulty of establishing an exact model of a micro turbine engine, a control algorithm based on neural network is proposed. The RBF neural network can identify the output value online, which is used by the single neuron controller to adjust its parameters based on a gradient algorithm. The simulation and rig test experiments show that,...
A Boolean network is a logical dynamic system, which has been used to describe cellular networks. Using a new matrix product, called semi-tensor product of matrices, a logical function can be expressed as an algebraic function. This expression can covert the Boolean networks into discrete-time linear dynamic systems. Similarly, the Boolean control networks can also be converted into discrete time...
A nondestructive optical method for determining the geographic origins of rice was investigated. Average absorption spectra of rice for three different geographic origins were analyzed. Direct orthogonal signal correction (DOSC), standard normal variate transformation (SNV) combined with detrending, multiply scatter calibration (MSC) and Savitzky-Golay second-order derivative transformation (S.Golay...
After study on the robust optimization of speech recognition system, we propose an improved wavelet thresholds de-noising method and combine it with the temporal filter to pre-enhance the noisy speech signals before recognition, which leads to good results. Then a hybrid model of hidden Markov and BP neural network is proposed, using BP to get the HMM (hidden Markov model) observation probability,...
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