System Identification of multi-rotor UAVs using echo state networks

Controller design for aircraft with unusual configurations presents unique challenges, particularly in extracting valid mathematical models of the MRUAVs behaviour. System Identification is a collection of techniques for extracting an accurate mathematical model of a dynamic system from experimental input-output data. This can entail parameter identification only (known as grey-box modelling) or more generally full parameter/structural identification of the nonlinear mapping (known as black-box). In this paper we propose a new method for black-box identification of the non-linear dynamic model of a small MRUAV using Echo State Networks (ESN), a novel approach to train Recurrent Neural Networks (RNN)

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

Global models of dynamic complex systems – modelling using the multilayer neural networks

In this paper, global models of dynamic complex systems using the neural networks isdiscussed. The description of a complex system is given by a description of each system elementand structure. As a model the multilayer neural networks with the tapped delay line (TDL), whichhave the same structure as a complex system, are accepted. Two approaches, a global model and aglobal model with the quality local model taken into account are proposed.To learn global models the modified back-propagation algorithms have been developed for theunique structure of the complex model. To model dynamic simple plants, of which the complexsystem is composed, a series-parallel model of identification using the feedforward network withthe tapped delay line (TDL) and the feedback loops, in which the gradient can be calculated bymeans of the simpler static back-propagation method is proposed. Computer simulations wereperformed for the dynamic complex system, which consists of two dynamic nonlinear simpleplants connected in series, described by means of nonlinear difference equations

Integrated evolutionary computation neural network quality controller for automated systems

With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time

Identification of cumulative fruit responses during storage using neural networks (Identifikasi Terhadap Respon Kumulatif Buah Selma Penyimpanan Dengan Metode Neural Netvork)

Neural networks are useful to identify complex nonlinear relationships between input and output of a system. Cumulative fruit responses such as water losses and ripening during storage are characterized non-linearly. For identification, several patterns of these cumulative responses, as affected by environmental factors, are often conducted by repealing the experiment several times under different environmental conditions. It is not well-known how many response patterns (training data sets) are necessary for an acceptable identification. This research explores an effective way to identify the cumulative responses of tomato during storage using neural networks. Firstly, data for identification were obtained from a mathematical model. Secondly, the relationship between the number of response pattern and the estimation error were investigated. The estimated error becomes smaller when the number of response pattern is three or more. This suggests that three types of response patterns allow cumulative responses to be successfully identified. Besides, an addition of linear data (1, 2, .., N) as input variable significantly improves the identification accuracy of the cumulative response. Finally, the identification of actual data was implemented based on these results and the satisfactory results will be obtained. Keywords: Storage process, dynamic system, cumulative fruit responses, identification, neural network

FPGA implementation of an image recognition system based on tiny neural networks and on-line reconfiguration

Neural networks are widely used in pattern recognition, security applications and robot control. We propose a hardware architecture system; using Tiny Neural Networks (TNN) specialized in image recognition. The generic TNN architecture allows expandability by means of mapping several Basic units (layers) and dynamic reconfiguration; depending on the application specific demands. One of the most important features of Tiny Neural Networks (TNN) is their learning ability. Weight modification and architecture reconfiguration can be carried out at run time. Our system performs shape identification by the interpretation of their singularities. This is achieved by interconnecting several specialized TNN. The results of several tests, in different conditions are reported in the paper. The system detects accurately a test shape in almost all the experiments performed. The paper also contains a detailed description of the system architecture and the processing steps. In order to validate the research, the system has been implemented and was configured as a perceptron network with backpropagation learning and applied to the recognition of shapes. Simulation results show that this architecture has significant performance benefits

Identification and control of dynamic systems using neural networks.

The aim of this thesis is to contribute in solving problems related to the on-line identification and control of unknown dynamic systems using feedforward neural networks. In this sense, this thesis presents new on-line learning algorithms for feedforward neural networks based upon the theory of variable structure system design, along with mathematical proofs regarding the convergence of solutions given by the algorithms; the boundedness of these solutions; and robustness features of the algorithms with respect to external perturbations affecting the neural networks' signals. In the thesis, the problems of on-line identification of the forward transfer operator, and the inverse transfer operator of unknown dynamic systems are also analysed, and neural networks-based identification schemes are proposed. These identification schemes are tested by computer simulations on linear and nonlinear unknown plants using both continuous-time and discrete-time versions of the proposed learning algorithms. The thesis reports about the direct inverse dynamics control problems using neural networks, and contributes towards solving these problems by proposing a direct inverse dynamics neural network-based control scheme with on-line learning capabilities of the inverse dynamics of the plant, and the addition of a feedback path that enables the resulting control scheme to exhibit robustness characteristics with respect to external disturbances affecting the output of the system. Computer simulation results on the performance of the mentioned control scheme in controlling linear and nonlinear plants are also included. The thesis also formulates a neural network-based internal model control scheme with on-line estimation capabilities of the forward transfer operator and the inverse transfer operator of unknown dynamic systems. The performance of this internal model control scheme is tested by computer simulations using a stable open-loop unknown plant with output signal corrupted by white noise. Finally, the thesis proposes a neural network-based adaptive control scheme where identification and control are simultaneously carried out

Neural network-based controllers for an electrothermal furnace system

Neural network schemes are applied in this thesis to a temperature control system problem. The electrothermal furnace is a very popular instrument for applications in material testing area. In this work feedforward neural networks are trained for both identification and control problems of the electrothermal furnace system. The thesis demonstrates that neural networks can be used effectively for this application problem, which is a highly nonlinear dynamical system. The first emphasis is on the electrothermal furnace model identification and the second emphasis is on the design of neural network based PID and internal model control strategies. Both static and dynamic back-propagation methods are discussed. In the electrothermal furnace models that are introduced, multi-layer feedforward networks are interconnected in novel configurations. A novel technique based on the internal model control for nonlinear systems using neural networks is proposed. The control structure proposed directly incorporates a model of the plant that was identified by a neural network and its inverse as part of the control strategy. The potential utilizations of the proposed methods are illustrated through experimental and numerical simulations of an electrothermal furnace system

Reconfigurable hardware architecture of a shape recognition system based on specialized tiny neural networks with online training.

Neural networks are widely used in pattern recognition, security applications, and robot control. We propose a hardware architecture system using tiny neural networks (TNNs)specialized in image recognition. The generic TNN architecture allows for expandability by means of mapping several basic units(layers) and dynamic reconfiguration, depending on the application specific demands. One of the most important features of TNNs is their learning ability. Weight modification and architecture reconfiguration can be carried out at run-time. Our system performs objects identification by the interpretation of characteristics elements of their shapes. This is achieved by interconnecting several specialized TNNs. The results of several tests in different conditions are reported in this paper. The system accurately detects a test shape in most of the experiments performed. This paper also contains a detailed description of the system architecture and the processing steps. In order to validate the research, the system has been implemented and configured as a perceptron network with back-propagation learning, choosing as reference application the recognition of shapes. Simulation results show that this architecture has significant performance benefits