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Software Defined Networking (SDN) is a new promising networking concept which has a centralized control over the network and separates the data and control planes. This new approach provides abstraction of lower-level functionality and allows the network administrators to initialize, control, change, and manage network behavior programmatically. The centralized control, being the major advantage of...
In this study, we investigate the control performance of an adaptive-type feedforward feedback controller using multilayer hypercomplex-valued neural network. The control system consists of a neural network and a feedback controller, whereby the control input of a plant is synthesised online by using the sum of the multilayer hypercomplex-valued neural network and the feedback controller to track...
The main tendency of rolling mills drives modernization is outdated analog control systems replacement with new digital ones. At the same time, a power section of the drive remains the same. This gives an opportunity to develop and implement adaptive digital control system for such plants in order to take into consideration their high nonlinearity. Having considered different types of such systems,...
The BP NN (back propagation neural network) stable controller for ball-beam system is designed in this paper. The controller is constructed by a three-layer forward neural network, and Fletcher-Reeves conjugate gradient algorithm is used to adjust net weights. The NN training samples for controller is obtained from root locus controller. After off-line trained, controller is put into on-line control...
Hexacopter is a type of multicopter that can be used to lift a heavy load, hence very convenient to be utilised in agricultural fields. As the consequence, however, the attitude control of this hexacopter is rather difficult compare with that of a quadcopter with four motors, due to gyroscopic effect of the additional motors and in its combination with the heavy loads. In this paper, we have developed...
In this paper, a new online learning algorithm is proposed to learn a data sample in hybrid mode. This new algorithm is developed and referred as Growing and Pruning — Fuzzy ARTMAP-radial basis function (GAP-FAM-RBF) neural network. In this algorithm, fuzzy ARTMAP (FAM) network learns from training samples and radial basis function (RBF) network provides viable solutions. The GAP-FAM-RBF that proposed...
Evaluation of Latin handwriting alphabet stroke formation is a very tedious and time consuming. This task is usually dependent on experts' subjective evaluation based on handwriting legibility criteria. This paper proposes to evaluate the correctness of stroke formation from letter decomposition. Only six complex straight line alphabet explicitly L, H, A, N, K, M are used. Each letter are decomposed...
In this study, Clifford multi-layer neural networks using a back-propagation algorithm are applied to control a nonlinear dynamic system to investigate its capability in practical control applications. A self-tuning feedback controller in which feedback gain parameters are adjusted by the Clifford multi-layer neural network is designed and a trail-based learning architecture is introduced in the online...
In this paper, a maximum sensibility neural network is proposed to make an online learning system of a inverse controller of a plant. This neural network is trained to learn the response of the plant to different random inputs. Once the network is trained, it can be used to control the plant to a desired output.
The BP neural network can combine with Levenberg-Marquardt algorithm, which effectively overcomes the disadvantages of traditional neural network such as slow convergence speed, always converging to local minimum point and poor stability. Neural network capture the data of inverted pendulum with LQR optimal control method, set up multilayer forward feedback neural network. We through matlab simulate...
Development of modern technologies is related to an increasing complexity of the objects controled and hence the systems controlling them. In the most cases, automatic control systems consist of different nonlinear elements that significantly limit the capabilities of classical control theory in designing controllers. In recent decades, the methodology of neural networks has been increasingly used...
In order to solve the problems existing in the fault diagnosis of tank fire control system, such as bigger subjectivity and less accuracy, a fault diagnosis model based on BP (Back Propagation) is studied. The working conditions of tank fire control system are described with a group of state parameters. A fault diagnosis model is established and a self adaptive variable BP learning algorithm is designed...
This paper presents AFCMAC, an Auto-adaptive Fuzzy Cerebellar Model Articulation Controller, and its comparison with the traditional CMAC (Cerebellar Model Articulation Controller) for horizontal voluntary eye movements. We evaluated the performance of the AFCMAC and the traditional CMAC, by using the standard deviation of binocular fixation disparity of five healthy control and five dyslexic subjects,...
In this work, a neural network (NN) based adaptive controller is developed and implemented for precise temperature control of a benchmark thermal system in cold climate. The newly devised NN controller is capable of overcoming the limitations of model dependent conventional fixed gain temperature controllers. The proposed NN controller is designed using the combination of off-line and on-line trainings...
This paper reviews a theory of robot motion planning and elaborates an application in which neural networks are used to take decisions regarding the orientation of a robot in its search for a destination target.The Simulink is used as a tool to implement the concepts. The neural networks allow obtaining a model of inverse dynamic, indeed the mathematical model of robot. The test was realized with...
The field of electronic noses and gas sensing has been developing rapidly since the introduction of the silicon based sensors. There are numerous systems that can detect and indicate the level of a specific gas. We introduce here a system that is low power, small and cheap enough to be used in mobile robotic platforms while still being accurate and reliable enough for confident use. The design is...
In many system control application areas, neural networks are often used to learn the inverse of a given system in terms of the system output information. The study of system invertibility becomes necessary for design of control systems incorporated with neural networks. A class of nonlinear dynamic systems, such as robotic systems, has been investigated in finding a lower bound of the system relative...
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