Identification of nonlinear dynamic systems using functional link artificial neural networks

Patra, J.C.; Pal, Rajarshi; Chatterji, B. N.; Panda, Ganapati

doi:10.1109/3477.752797

Cited by 338 publications

(171 citation statements)

References 13 publications

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“…Due to the absence of hidden layer in FLANN, it is computationally inexpensive. The characteristic of less computational complexity by FLANN than that by MLP can also be found in [13].…”

Section: Resultsmentioning

confidence: 96%

“…This experiment compares the computational complexities of FLANN [13] and MLP [4]. The computational complexity is reflected by calculating the average execution time of one iteration during training process.…”

Section: Methodsmentioning

confidence: 99%

“…The number of additions, multiplications, tanh(.) and trigonometric expansions can also be evaluated as in [13]. Here, we consider a two-layer MLP with {2 -8 -1} and a FLANN with {16 -1} architecture.…”

Section: Methodsmentioning

confidence: 99%

“…Irradiance and temperature are the inputs of the PV arrays. The functional expansion block is used to transfer the input variable into a higher dimension space [13]. The expanded inputs are connected to the output layer, which is activated by a hyperbolic tangent transfer function.…”

Section: Principle Of the Flannmentioning

confidence: 99%

See 3 more Smart Citations

A flann-based controller for maximum power point tracking in PV systems under rapidly changing conditions

Jiang

Maskell

Patra

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In order to increase the efficiency of the Photovoltaic (PV) system, the PV system should be operated at the Maximum Power Point (MPP). The MPP Tracking (MPPT) is an essential part in achieving this improvement. Some of the existing techniques such as Perturb-and-Observe (P&O) and Incremental Conductance (INC) are relatively simpler to implement, but under rapidly changing irradiance and temperature conditions, they fail to track the MPP. Although methods such as Multilayer Perceptron (MLP) and Fuzzy Logic (FL) are efficient in tracking the MPP, their implementation increases the system complexity. In this paper, we propose a novel artificial intelligence based controller for MPPT, which can efficiently track the MPP, while keeping the computational complexity within the limits. Our technique uses Functional Link Artificial Neural Network (FLANN) to predict the PV output voltage at the MPP. Since there is no hidden layer, FLANN is computationally inexpensive. Simulation results verify that the proposed FLANN controller is computationally less intensive and exhibits higher efficiency under rapidly changing weather conditions.

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“…Due to the absence of hidden layer in FLANN, it is computationally inexpensive. The characteristic of less computational complexity by FLANN than that by MLP can also be found in [13].…”

Section: Resultsmentioning

confidence: 96%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Principle Of the Flannmentioning

confidence: 99%

See 2 more Smart Citations

A flann-based controller for maximum power point tracking in PV systems under rapidly changing conditions

Jiang

Maskell

Patra

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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show abstract

“…Mostly, various neural networks models are used by researchers [14]- [19] for the accurate representation of the nonlinear systems which has the advantage of its generalization and learning ability [18]. In recent years, use of multi-layer perceptron (MLP), radial basis function (RBF), recurrent and simultaneous recurrent neural network (RNN and SRN) has been reported for online estimation of input−output mapping of nonlinear systems [18], [20]- [22]. These methods typically use back-propagation (BP) or back-propagation through time (BPTT) to update online the neural network parameters.…”

Section: Introductionmentioning

confidence: 99%

Multivariable Self-Tuning Feedback Linearization Controller for Power Oscillation Damping

Arif

Ray

Chaudhuri

2014

IEEE Trans. Contr. Syst. Technol.

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Abstract-The objective of this paper is to design a measurement based self-tuning controller which does not rely on accurate models and deals with nonlinearities in system response. A special form of neural network (NN) model called as feedback linearizable neural network (FLNN) compatible with feedback linearization technique is proposed for representation of nonlinear power systems behaviour. Levenberg−Marquardt (LM) is applied in batch mode to improve the model estimation. A time varying feedback linearization controller (FBLC) is employed in conjunction with the FLNN−LM estimator to generate the control signal. Validation of the performance of proposed algorithm is done through the modeling and simulating both normal and heavy loading of transmission lines, when the nonlinearities are pronounced. Case studies on a large scale 16−machine, 5−area power system are reported for different power flow scenarios, to prove the superiority of proposed scheme against a conventional model based controller. A coefficient vector Λ for FBLC is derived and utilized online at each time instant, to enhance the damping performance of controller.

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Development of a novel robust identification scheme for nonlinear dynamic systems

George

Panda

2014

Adaptive Control & Signal

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This paper presents a set of single layer low complexity nonlinear adaptive models for efficient identification of dynamic systems in the presence of outliers in the training signal. The weights of the new models have been updated using a new robust learning algorithm. The proposed robust algorithm is based on adaptive minimization of Wilcoxon norm of errors. The computational complexity associated with the new models has further been reduced by processing the input in block form and using a newly derived robust block learning algorithm. Through exhaustive simulation study of many benchmark identification examples, it has been shown that in all cases, the new models provide enhanced and robust identification performance compared with that provided by the corresponding conventional squared error-based approaches. Copyright by temporary or permanent breakdown of sensors, analog-to-digital conversion errors, failure of transducers, and so on.In the recent past, many robust learning algorithms, which are efficient in learning in the presence of outliers, have been proposed in the literature. A robust recursive least square method has been developed for identification of bilinear systems [18]. Another robust learning algorithm, which employs a soft filtering approach to remove the most prominent outliers before parameter estimation, has been derived in [19]. The technique has been applied to develop a robust recurrent neural network. Sanchez [20] has proposed a robust learning method for RBF networks, in which an RBF architecture is generated in the first phase using conventional learning scheme, and subsequently, a robust learning method is introduced to make the network robust against outliers. The mechanism by which outliers affect neural network models has been studied in [21] using the analysis of its influence function. The author also suggested the need for considering the use of error distribution functions with extensive tails, like the Cauchy or Lorentzian distribution. A mean log squared error cost function has also been proposed, which is more robust compared with conventional least mean square approach. Other prominent robust learning techniques include random sample consensus method [22] and M -estimator sampling consensus method [23].By combining conventional robust techniques with optimization of learning rate, a robust learning algorithm for optimizing fuzzy neural networks has been suggested in [24]. A robust fuzzy regression agglomeration clustering algorithm has been presented in [25] to develop Takagi-Sugeno-Kang models, which are robust against outliers. They have tested the performance through simulation studies. A hybrid robust Takagi-Sugeno-Kang modeling approach has been recently proposed in [26] by using a robust fuzzy C-regression model clustering algorithm in the coarse-tuning stage and an annealing robust back propagation learning algorithm in the fine-tuning phase. A robust support vector regression network has been proposed by employing traditional robust learning approaches to improve ...

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Identification of nonlinear dynamic systems using functional link artificial neural networks

Cited by 338 publications

References 13 publications

A flann-based controller for maximum power point tracking in PV systems under rapidly changing conditions

A flann-based controller for maximum power point tracking in PV systems under rapidly changing conditions

Multivariable Self-Tuning Feedback Linearization Controller for Power Oscillation Damping

Development of a novel robust identification scheme for nonlinear dynamic systems

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