Nonlinear System Identification with Unknown Piecewise Time-Varying Delay

Identification of nonlinear systems with unknown piecewise time-varying delay is concerned in this paper.Multiple auto regressive exogenous(ARX) models are identified at different process operating points,and the complete dynamics of the nonlinear system is represented by using a combination of a normalized exponential function as the probability density function with each of the local models.The parameters of the local ARX models and the exponential functions as well as the unknown piecewise time-varying delays are estimated simultaneously under the framework of the expectation maximization(EM) algorithm.A simulation example is applied to demonstrating the proposed identification method.

An Improved Differential Evolution Trained Neural Network Scheme for Nonlinear System Identification

This paper presents an improved nonlinear system identification scheme using di？erential evolution （DE）, neural network （NN） and Levenberg Marquardt algorithm （LM）. With a view to achieve better convergence of NN weights optimization during the training, the DE and LM are used in a combined framework to train the NN. We present the convergence analysis of the DE and demonstrate the efficacy of the proposed improved system identification algorithm by exploiting the combined DE and LM training of the NN and suitably implementing it together with other system identification methods, namely NN and DE＋NN on a number of examples including a practical case study. The identification results obtained through a series of simulation studies of these methods on different nonlinear systems demonstrate that the proposed DE and LM trained NN approach to nonlinear system identification can yield better identification results in terms of time of convergence and less identification error.

Nonlinear Dynamic System Identification of ARX Model for Speech Signal Identification

System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems.As most practical systems don’t have prior information about the system behaviour thus,mathematical modelling is required.The authors have proposed a stacked Bidirectional Long-Short Term Memory(Bi-LSTM)model to handle the problem of nonlinear dynamic system identification in this paper.The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions.The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways:one from the past to the future,and the other from the future to the past.In this proposed model a backward-running Long-Short Term Memory(LSTM)can store information from the future along with application of two hidden states together allows for storing information from the past and future at any moment in time.The proposed model is tested with a recorded speech signal to prove its superiority with the performance being evaluated through Mean Square Error(MSE)and Root Means Square Error(RMSE).The RMSE and MSE performances obtained by the proposed model are found to be 0.0218 and 0.0162 respectively for 500 Epochs.The comparison of results and further analysis illustrates that the proposed model achieves better performance over other models and can obtain higher prediction accuracy along with faster convergence speed.

An Improved SPSA Algorithm for System Identification Using Fuzzy Rules for Training Neural Networks

Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.

Nonlinear system modeling and identification of small helicopter based on genetic algorithm

Purpose–The purpose of this paper is to build nonlinear model of a small rotorcraft-based unmanned aerial vehicles(RUAV),using nonlinear system identification method to estimate the parameters of the model.The nonlinear model will be used in robust control system design and aerodynamic analysis.Design/methodology/approach–The nonlinear model is built based on mechanism theory,aerodynamics and mechanics,which can reflect most dynamics in large flight envelop.Genetic algorithm(GA)and time domain flight data is adopted to estimate unknown parameters of the model.The flight data were collected from a series of fight tests.The identification results were also analyzed and validated.Findings–The nonlinear model of RUAV has better accuracy,the parameters are physical quantities,and having distinctly recognizable values.The GA is suitable for nonlinear system identification.And the results proved the identified model can reflect the dynamic characteristics in extensive area of flight envelop.Research limitations/implications–The GA requires much more computing power,to identify 12 unknown parameters with 30 iterations,will takes more than 18 hours of a four cores desktop computer.Because of this is an off-line identification process,and has more accuracy,extra time is acceptable.Originality/value–GA method has significantly increased the accuracy of the model.The previous work of system identification used a ten states linear model,and using PEM identified 23 coefficients.By carefully building the nonlinear model,it has only 21 unknown parameters,but if the model is linearized,it will get a linear model more than 35 states,which shows nonlinear model contain more dynamics than linear model.

Generalized Multiscale RBF Networks and the DCT for Breast Cancer Detection

The use of the multiscale generalized radial basis function(MSRBF)neural networks for image feature extraction and medical image analysis and classification is proposed for the first time in this work.The MSRBF networks hold a simple and flexible architecture that has been successfully used in forecasting and model structure detection of input-output nonlinear systems.In this work instead,MSRBF networks are part of an integrated computer-aided diagnosis(CAD)framework for breast cancer detection,which holds three stages:an input-output model is obtained from the image,followed by a high-level image feature extraction from the model and a classification module aimed at predicting breast cancer.In the first stage,the image data is rendered into a multiple-input-single-output(MISO)system.In order to improve the characterisation,the nonlinear autoregressive with exogenous inputs(NARX)model is introduced to rearrange the available input-output data in a nonlinear way.The forward regression orthogonal least squares(FROLS)algorithm is then used to take advantage of the previous arrangement by solving the system as a model structure detection problem and finding the output layer weights of the NARX-MSRBF network.In the second stage,once the network model is available,the feature extraction takes place by stimulating the input to produce output signals to be compressed by the discrete cosine transform(DCT).In the third stage,we leverage the extracted features by using a clustering algorithm for classification to integrate a CAD system for breast cancer detection.To test the method performance,three different and well-known public image repositories were used:the mini-MIAS and the MMSD for mammography,and the BreaKHis for histopathology images.A comparison exercise was also made between different database partitions to understand the mammogram breast density effect in the performance since there are few remarks in the literature on this factor.Classification results show that the new CAD method reached an accuracy of 93.5%in mini-Mammo graphic image analysis society(mini-MIAS),93.99%in digital database for screening mammography(DDSM)and 86.7%in the BreaKHis.We found that the MSRBF networks are able to build tailored and precise image models and,combined with the DCT,to extract high-quality features from both black and white and coloured images.