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A new scheme for adaptive neural networks for nonlinear dynamic system identification is proposed in this paper. The network of structure multi-layer perceptron with external recurrence is trained offline at first to get the initial network parameters. The parameters of the network are classified into short-term memory part and long-term memory part. The short-term memory part includes the parameters...
This study presents a new technique for identifying nonlinear systems using multiple models. In this technique the identification structure used is ANFIS, consequent parts are performed by multiple models and the interpolation of local models is performed by the membership functions of the Takagi Sugeno fuzzy system. The identification technique uses a number of multiple model concepts to initiate...
This paper presents a new approach for identifying and validating the F/A-18 aeroservoelastic model, based on flight flutter tests. The neural network (NN), trained with five different flight flutter cases, is validated using 11 other flight flutter test (FFT) data. A total of 16 FFT cases were obtained for all three flight regimes (subsonic, transonic, and supersonic) at Mach numbers ranging between...
In this work a nonlinear identification approach has been developed and implemented on Alstom gasifier with Wiener model. The linear element of the Wiener model is identified by a combined subspace state space method, which integrates MOESP (Multivariable Output-Error State Space) and N4SID (Numerical algorithms for subspace state space system identification) method in the estimation of system matrices...
Design and development of unmanned aerial vehicles has attracted increased interest in the recent past. Rotorcraft UAVs, in particular have more challenges than its fixed wing counterparts. More research and experiments have been conducted to study the stability and control of RUAVs. A model-based control system design is particularly of our interest since it avoids a tedious trial and error process...
A method for modeling of the linear dynamic systems with input hysteresis is proposed. Considering hysteresis involved in the system is a non-smooth and multi-valued nonlinearity, the generalized gradient of the output with respect to the input of the nonlinear system is introduced to extract the movement tendency of the system. Then, the generalized gradient is included into the expanded input space,...
PID neural network (PID-NN) is a new type of dynamic feed-forward network which combines neural network with PID control strategy. It performs a perfect function in process control with the merit of both general PID controller and neural network. In this paper, the concepts of variable integral and partial differential are introduced in the design of hidden-layer of PID-NN to improve the capabilities...
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