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Characteristic features of feedforward artificial neural networks, acting as universal function approximators, are presented. The problem under consideration concerns inverse kinematics of a two-link planar manipulator. As shown in the article, a two-layer, feedforward neural network is able to learn the nonlinear mapping between the end-effector position domain and the joint angle domain of the manipulator...
The BP Deepwater Horizon oil spill disaster has reminded us, once again, the potential for systemic failures in complex engineered systems. But such systemic failures are not limited to the chemical and petrochemical industries alone. The 2003 Northeast electrical power blackout was a systemic failure. Financial disasters such as Enron, WorldCom, subprime derivatives market, and so on, also belong...
To maintain an efficient operating unit and avoid failure of mineral critical equipment, fault detection and diagnosis are the solution for the critical parts of these equipments. This paper presents a non parametric classification technique using Best Selective Parameters (BSP) embedded in Binary Decision Tree (BDT). The method arises from the question: how can we choose suitable types of parameters...
This paper presents an identification method of Multi-Input Multi-Output (MIMO) dynamic systems based on the Group Method of Data Handing. In particular, a new structure of the dynamic neuron is proposed. The synthesis of the neural model is performed with the application of the pole estimation approach. The final part of this work contains an illustrative example regarding the application of the...
Nowadays, fault diagnosis systems design should have into account the distribution and complexity of the process and must be able to cooperate and communicate with other systems to achieve satisfactory performance. To this end a new agent based fault detection system for networked control process is proposed in this paper. A hybrid architecture based on horizontal layers as fault detection (FD) agents...
This paper provides an adaptation algorithm for the control of complex system via recurrent neural networks. The proposed method is derived from RTRL algorithm. Neural emulator and neural controller parameters are one-line updated independently. To illustrate the tracking and the disturbance rejection capabilities of the real time control algorithm and the efficiency of the networks parameters relaxation,...
This paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as an one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are investigated. A set of 12 faulty scenarios...
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