Applications of Brain-Inspired SOR Network to Controller Design and Knowledge Acquisition
نویسندگان
چکیده
In this paper, we propose the SOR network with fuzzy inference based evaluation inspired by brain function. In the proposed method, controller design and knowledge acquisition are achieved simultaneously. All the designer has to do is to describe evaluation rules for the input/output data set sampled by trial and error. In the description process, only designer’s commonsense knowledge is required. SOR network extracts practical knowledge from the data set with evaluation, and works as a fuzzy controller after the learning.
منابع مشابه
A Biologically Inspired Neural Network for Autonomous Underwater Vehicles
Autonomous underwater vehicles (AUVs) have great advantages for activities in deep oceans, and are expected as the attractive tool for near future underwater development or investigation. However, AUVs have various problems which should be solved for motion control, acquisition of sensors’ information, behavioral decision, navigation without collision, self-localization and so on. This paper pr...
متن کاملDesigninga Neuro-Sliding Mode Controller for Networked Control Systems with Packet Dropout
This paper addresses control design in networked control system by considering stochastic packet dropouts in the forward path of the control loop. The packet dropouts are modelled by mutually independent stochastic variables satisfying Bernoulli binary distribution. A sliding mode controller is utilized to overcome the adverse influences of stochastic packet dropouts in networked control system...
متن کاملDesign and Practical Implementation of a New Markov Model Predictive Controller for Variable Communication Packet Loss in Network Control Systems
The current paper investigates the influence of packet losses in network control systems (NCS’s) using the model predictive control (MPC) strategy. The study focuses on two main network packet losses due to sensor to controller and controller to actuator along the communication paths. A new Markov-based method is employed to recursively estimate the probability of time delay in controller to ac...
متن کاملAdaptive fuzzy sliding mode and indirect radial-basis-function neural network controller for trajectory tracking control of a car-like robot
The ever-growing use of various vehicles for transportation, on the one hand, and the statistics ofsoaring road accidents resulting from human error, on the other hand, reminds us of the necessity toconduct more extensive research on the design, manufacturing and control of driver-less intelligentvehicles. For the automatic control of an autonomous vehicle, we need its dynamic...
متن کاملHybrid Adaptive Neural Network AUV controller design with Sliding Mode Robust Term
This work addresses an autonomous underwater vehicle (AUV) for applying nonlinear control which is capable of disturbance rejection via intelligent estimation of uncertainties. Adaptive radial basis function neural network (RBF NN) controller is proposed to approximate unknown nonlinear dynamics. The problem of designing an adaptive RBF NN controller was augmented with sliding mode robust term ...
متن کامل