PSO Assisted NURB Neural Network Identification
نویسندگان
چکیده
A system identification algorithm is introduced for Hammerstein systems that are modelled using a non-uniform rational B-spline (NURB) neural network. The proposed algorithm consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a linear pole assignment controller are utilized to demonstrate the efficacy of the proposed approach.
منابع مشابه
The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization
In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the...
متن کاملArtificial Intelligence Based Approach for Identification of Current Transformer Saturation from Faults in Power Transformers
Protection systems have vital role in network reliability in short circuit mode and proper operating for relays. Current transformer often in transient and saturation under short circuit mode causes mal-operation of relays which will have undesirable effects. Therefore, proper and quick identification of Current transformer saturation is so important. In this paper, an Artificial Neural Network...
متن کاملIdentification of Wind Turbine using Fractional Order Dynamic Neural Network and Optimization Algorithm
In this paper, an efficient technique is presented to identify a 2500 KW wind turbine operating in Kahak wind farm, Qazvin province, Iran. This complicated system dealing with wind behavior is identified by using a proposed fractional order dynamic neural network (FODNN) optimized with evolutionary computation. In the proposed method, some parameters of FODNN are unknown during the process of i...
متن کاملFlaw Identification of Metal Material in Eddy Current Testing Using Neural Network Optimized by Particle Swarm Optimization
As an NDT technology, Eddy current testing is widely used to identify the surface flaw of metal material. However, due to the complex relationship between the test results and the flaw’s shape, the identification is qualitative in most situations. In the paper, a neural network optimized by particle swarm optimization (PSO) is used to quantify the detection result tentatively of the fault on th...
متن کاملParticle Swarm Optimization and Neural Network for Frequency Domain Identification of Servo System with Friction Force
Generally, the mechanical devices come with undesirable nonlinearities. Due to these nonlinearities the frequency domain system identification process in servo system seems to be a tough task. In the paper, particle swarm optimization (PSO) algorithm based hybrid technique is proposed for the frequency domain identification of servo system. The proposed hybrid technique is the combination of ar...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2012