Modelling and inverting complex-valued Wiener systems

We develop a complex-valued (CV) B-spline neural network approach for efficient identification and inversion of CV Wiener systems. The CV nonlinear static function in the Wiener system is represented using the tensor product of two univariate B-spline neural networks. With the aid of a least squares parameter initialisation, the Gauss-Newton algorithm effectively estimates the model parameters that include the CV linear dynamic model coefficients and B-spline neural network weights. The identification algorithm naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. An accurate inverse of the CV Wiener system is then obtained, in which the inverse of the CV nonlinear static function of the Wiener system is calculated efficiently using the Gaussian-Newton algorithm based on the estimated B-spline neural network model, with the aid of the De Boor recursions. The effectiveness of our approach for identification and inversion of CV Wiener systems is demonstrated using the application of digital predistorter design for high power amplifiers with memor

Sawtooth Genetic Algorithm and its Application in Hammerstein Model identification and RBFN based stock Market Forecasting

This Project work has been divided into three parts. In the first part, we deal with the sawtooth genetic algorithm. In the second part, we use this algorithm for optimization of Hammerstein model. In the third part we implemented a stock market forecasting model based on radial basis function network tuned by sawtooth genetic algorithm

Identification of Nonlinear Systems From the Knowledge Around Different Operating Conditions: A Feed-Forward Multi-Layer ANN Based Approach

The paper investigates nonlinear system identification using system output data at various linearized operating points. A feed-forward multi-layer Artificial Neural Network (ANN) based approach is used for this purpose and tested for two target applications i.e. nuclear reactor power level monitoring and an AC servo position control system. Various configurations of ANN using different activation functions, number of hidden layers and neurons in each layer are trained and tested to find out the best configuration. The training is carried out multiple times to check for consistency and the mean and standard deviation of the root mean square errors (RMSE) are reported for each configuration.Comment: "6 pages, 9 figures; The Second IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC-2012), December 2012, Solan

Weiner Model Drop Test Identification of a Light Amphibious Airplane

The new approach of the Weiner model for identifying drop test dynamics of a light amphibious airplane is presented in this paper. Unlike the traditional identification method of the Hammerstein model using LS-SVM with Gaussian radial basis serving as the kernel function, the small-signal excitation input is used to estimate the linear block of the Weiner model. Then, the static nonlinearity function of the model is identified through LS-SVM. The RMSE of the proposed Weiner model is 0.48805 and 0.38246 for the strut and wheel of the landing gear. The proposed Weiner model has better identification performance than the Hammerstein model and the traditional governing equation of the landing gear. The drop experiment of the light amphibious airplane is carried out not only to prove standard airworthiness compliance but also to verify the identifiability, accuracy, and performance of system identification

A comparative study of surrogate musculoskeletal models using various neural network configurations

Title from PDF of title page, viewed on August 13, 2013Thesis advisor: Reza R. DerakhshaniVitaIncludes bibliographic references (pages 85-88)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2013The central idea in musculoskeletal modeling is to be able to predict body-level (e.g. muscle forces) as well as tissue-level information (tissue-level stress, strain, etc.). To develop computationally efficient techniques to analyze such models, surrogate models have been introduced which concurrently predict both body-level and tissue-level information using multi-body and finite-element analysis, respectively. However, this kind of surrogate model is not an optimum solution as it involves the usage of finite element models which are computation intensive and involve complex meshing methods especially during real-time movement simulations. An alternative surrogate modeling method is the use of artificial neural networks in place of finite-element models. The ultimate objective of this research is to predict tissue-level stresses experienced by the cartilage and ligaments during movement and achieve concurrent simulation of muscle force and tissue stress using various surrogate neural network models, where stresses obtained from finite-element models provide the frame of reference. Over the last decade, neural networks have been successfully implemented in several biomechanical modeling applications. Their adaptive ability to learn from examples, simple implementation techniques, and fast simulation times make neural networks versatile and robust when compared to other techniques. The neural network models are trained with reaction forces from multi-body models and stresses from finite element models obtained at the interested elements. Several configurations of static and dynamic neural networks are modeled, and accuracies close to 93% were achieved, where the correlation coefficient is the chosen measure of goodness. Using neural networks, the simulation time was reduced nearly 40,000 times when compared to the finite-element models. This study also confirms theoretical concepts that special network configurations--including average committee, stacked generalization, and negative correlation learning--provide considerably better results when compared to individual networks themselves.Introduction -- Methods -- Results -- Conclusion -- Future work -- Appendix A. Various linear and non-linear modeling techniques -- Appendix B. Error analysi

Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. A new efficient Common Model Structure Selection (CMSS) algorithm is proposed to select a common model structure. The main idea and key procedure is: First, generate K 1 data sets (the first K data sets are used for training, and theK 1 th one is used for testing) using an online sliding window method; then detect significant model terms to form a common model structure which fits over all the K training data sets using the new proposed CMSS approach. Finally, estimate and refine the time-varying parameters for the identified common-structured model using a Recursive Least Squares (RLS) parameter estimation method. The new method can effectively detect and adaptively track the transient variation of nonstationary signals. Two examples are presented to illustrate the effectiveness of the new approach including an application to an EEG data set

Advancing block-oriented modeling in process control

The increasing pressure in industry to maintain tight control over processes has led to the development of many advanced control algorithms. Many of these algorithms are model-based control schemes, which require an accurate predictive model of the process to achieve good controller performance. Because of this, research in the fields of nonlinear process modeling and predictive control has advanced over the past several decades.;In this dissertation, a new method for identifying complicated block-oriented nonlinear models of processes will be proposed. This method is applied for LNL and LLN sandwich block-oriented models and will be shown to accurately predict process response behavior for a simulated continuous-stirred tank reactor (CSTR) and a pilot-scale distillation column. In addition, it will be shown to effectively model the pilot-scale distillation column using closed-loop, highly correlated input data.;Using the block-oriented models identified, a new feedforward control framework has been developed. This feedforward control framework represents the first that compensates for multiple input disturbances occurring simultaneously. Only a single process model is needed to account for all measured disturbances. In addition, it allows a plant engineer to develop the predictive model of the process from plant historical data instead of introducing a series of disturbances to the process to try to identify the model. This has the potential to considerably reduce the cost of implementing an advanced control scheme in terms of time, effort and money. The proposed feedforward control framework is tested on a simulated CSTR process in Chapter 4, and on a pilot-scale distillation column in Chapter 5