Morning Camp Solution

A genetic neural network approach to identifying reaction coordinates

2009-04-28T06:18:00.000-07:00

Aaron DinnerDepartment of ChemistryUniversity of ChicagoRI 340E, 5640 S Ellis AveChicago, IL 60637USATo interpret simulations of a complex system to determine the physical mechanism of a dynamical process, it is necessary to identify the small number of coordinates that distinguish the stable states from the transition states. We develop an automatic method for identifying these degrees of

Evolving Recurrent Neural Networks with Non binary Encoding

2009-04-28T06:15:00.000-07:00

Martin MandischerSystems Analysis Research GroupDepartment of Computer ScienceUniversity of DortmundDortmund GermanyThis paper presents an evolutionary approach for the design of feed forward and recurrent neural networks We show that Evolutionary Algorithms can be used for the construction of networks for real world tasks Therefore a data structure based genotypic network representation as well

Integration of Genetic Algorithm and Neural Network for Financial Early Warning System: An Example of Taiwanese Banking Industry

2009-04-28T06:11:00.000-07:00

Jih-Chang Hsieh(1), Pei-Chann Chang(2), Shih-Hsin Chen(2)1. Department of Finance, Vanung University, Chung-Li 32061, Tao-Yuan, Taiwan.2. Department of Industrial Engineering and Management, Yuan-Ze University, Chung-Li,Tao-Yuan, Taiwan.Genetic algorithm and neural network (GNN) are integrated to build a financial early warning system. An example of Taiwanese banking industry is discussed to test

Feedback Approximation of the Stochastic Growth Model by Genetic Neural Networks

2009-04-28T06:08:00.000-07:00

Sibel Sirakaya(a) Stephen Turnovsky(b);y M. Nedim Alemdar(c)a. Departments of Economics and Statistics, and CSSS,University of Washington, Seattle, WA 98195b. Department of Economics, University of Washington, Seattle, WA 98195c. Department of Economics, Bilkent University, 06800 Bilkent, Ankara, TurkeyA direct numerical optimization method is developed to approximate the one-sector stochastic

MASSP: A hybrid genetic-neural system for predicting protein secondary structure

2009-04-28T06:03:00.000-07:00

Armano G.(1), Mancosu G.(2), Orro A.(1), Saba M.(1), and Vargiu E.(1)(1) DIEE - Dept. of Electrical and Electronic Engineering, University of Cagliari Piazza d’Armi, I-09123Cagliari, Italy (2) Shardna Life Sciences, Piazza Deffenu 4, I-09121 Cagliari, ItalyBeing the prediction of protein structure a very complex task, most methodologies concentrate on the simplified task of predicting secondary

GENETIC NEURAL NETWORK BASED DATA MINING AND APPLICATION IN CASE ANALYSIS OF POLICE OFFICE

2009-04-28T06:01:00.000-07:00

LIU Han-li, LI Lin, ZHU Hai-hongSchool of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan, P.R.China, 430079Tel: 86-27-87882209 E-mail: liuhl000@sohu.com; lilin@telecarto.comThis paper puts forward a method that combines the learning algorithm of BP neural network with genetic algorithm to train BP network and optimize the weight values of the network in a global scale.

BROADBAND MODELING OF WIRE ANTENNAS BY GENETIC NEURAL NETWORKS

2009-04-28T05:58:00.000-07:00

Abstract. The paper describes an approach to learning artificial neural networks (ANN) with the genetic algorithm (GA). Two types of ANN are used: the recurrent Elman ANN and the feed-forward one. Neural networks are implemented in MATLAB. Results of training abilities are discussed.

A HYBRID GENETIC-NEURAL FRONT-END EXTENSION FOR ROBUST SPEECH RECOGNITION OVER TELEPHONE LINES

2009-04-28T05:54:00.000-07:00

Sid-Ahmed Selouani*, Habib Hamam**, Douglas O’Shaughnessy***This paper presents a hybrid technique combining the Karhonen-Loeve Transform (KLT), the Multilayer Perceptron (MLP) and Genetic Algorithms (GAs) to obtain less-variant Mel-frequency parameters. The advantages of such an approach are that the robustness can be reached without modifying the recognition system, and that neither assumption

NEURO FUZZY CONTROL OF THE FES ASSISTED FREELY SWINGING LEG OF PARAPLEGIC SUBJECTS

2009-04-28T01:12:00.000-07:00

Jaap H. van der Spek, Wubbe J. R. Velthuis, Peter H. Veltink, Theo J. A. de Vries Faculty of Electrical Engineering, University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands, mail to: J.H.vanderspek@student.utwente.nlWe designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three ‘swing phase

Fuzzy Control of Micro-Spot Laser Welding

2009-04-28T01:09:00.000-07:00

Dipl.-Ing. Jan-Oliver BrasselChair of Manufacturing TechnologyUniversity of Erlangen-NurembergEgerlandstraße 11D–91058 ErlangenTel. +49 (0)9131 – 85 2 8326J.Brassel@terra.lft.uni-erlangen.deAlong the industrial laser welding of electronic devices the number of components produced, often reaches several thousand parts per day. With the goal to reduce failure costs to a minimum, online control of

Kinematics and fuzzy control of ISOGLIDE3 medical parallel robot

2009-04-28T01:04:00.000-07:00

The artificial intelligence techniques have been recognized approach to adaptation and conversion of human knowledge into a form understandable by computers. Advanced control based on artificial intelligence techniques is typically referred to as intelligent control.Medical parallel robots, on the other hand, have gained much popularity because of their:• intimate interaction with humans;•

Modified Fuzzy PID Control for Networked Control Systems with Random Delays

2009-04-28T00:55:00.000-07:00

Yong-can Cao and Wei-dong ZhangTo deal with random delays in Networked Control System (NCS), Modified Fuzzy PID Controller is introduced in this paper to implement real-time control adaptively. Via adjusting the control signal dynamically, the system performance is improved. In this paper, the design process and the ultimate simulation results are represented. Finally, examples and corresponding

Adaptive Sliding Mode Control with PID Tuning for Uncertain Systems

2009-04-28T00:51:00.000-07:00

T. C. Kuo, Member, IAENG, Y. J. Huang, Member, IAENG, C. Y. Chen, and C. H. ChangThis paper proposes a novel adaptive sliding mode control with PID tuning method for a class of uncertain systems. The goal is to achieve system robustness against parameter variations and external disturbances. Suitable PID control gain parameters can be systematically on-line computed according to the developed

NONLINEAR PID CONTROLLER MODIFICATION OF THE ELECTROMECHANICAL ACTUATOR SYSTEM FOR AEROFIN CONTROL WITH A PWM CONTROLLED DC MOTOR

2009-04-28T00:48:00.000-07:00

Milan R. Ristanović, Dragan V. Lazić, Ivica InđinFaculty of Mechanical Engineering, University of Belgrade, Kraljice Marije 16,11120 Belgrade, SerbiaNimico DOO, Djure Djakovića 8, 11080 Belgrade, SerbiaAbstract. In this paper, we investigate the control of an electromechanical actuator (EMA) system for aerofin control (AFC). The EMA is realized with permanent magnet brush DC motor controlled by

A PSO-Based Optimum Design of PID Controller for a Linear Brushless DC Motor

2009-04-28T00:44:00.000-07:00

Mehdi Nasri, Hossein Nezamabadi-pour, and Malihe MaghfooriThis Paper presents a particle swarm optimization (PSO) method for determining the optimal proportional-integralderivative (PID) controller parameters, for speed control of a linear brushless DC motor. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational

Design of FPGA based PID-like Fuzzy Controller for Industrial Applications

2009-04-28T00:40:00.000-07:00

Dr. Mohammed Y. Hassan, Member, IAENG Waleed F. Sharif, M. Sc.Control and Systems Engineering DepartmentUniversity of TechnologyBaghdad, IraqAbstract—This paper proposes the design of PID-like fuzzy logic controller (PIDFLC), on Field Programmable Gate Array (FPGA) device. The Fuzzy Inference System (FIS) used in the controller is aided with Active Rules Selection Mechanism. Developments were

PID Control of DAE Systems

2009-04-28T00:31:00.000-07:00

Nammi Madhusudana Rao, Prashant Vora, Kannan M. MoudgalyaDepartment of Chemical Engineering, Systems and Control GroupIndian Institute of Technology, Bombay, Powai, Mumbai 400 076, IndiaIt is shown through two nontrivial examples that it is possible to control DAE systems with PID controllers. A two phase reactor and a CSTR with a heating jacket are linearized about their operating points and

Introduction to Control Systems

2008-04-22T17:33:00.000-07:00

INTRODUCTIONA control system is a collection of components working together under the direction of some machine intelligence. In most cases, electronic circuits provide the intelligence, and electromechanical components such as sensors and motors provide

Transfer Function

2008-04-22T17:32:00.002-07:00

Physically, a control system is a collection of components and circuits connected together to perform a useful function. Each component in the system converts energy from one form to another; for example, we might think of a temperature sensor as converting degrees to volts or a motor as converting volts to revolutions per minute. To describe the performance of the entire control system, we must

ANALOG AND DIGITAL CONTROL SYSTEMS

2008-04-22T17:32:00.001-07:00

In an analog control system, the controller consists of traditional analog devices and circuits, that is, linear amplifiers. The first control systems were analog because it was the only available technology. In the analog control system, any change in either set point or feedback is sensed immediately, and the amplifiers adjust their output (to the actuator) accordingly.In a digital control

CLASSIFICATIONS OF CONTROL SYSTEMS - Process Control

2008-04-22T17:31:00.001-07:00

Process ControlProcess control refers to a control system that oversees some industrial process so that a uniform, correct output is maintained. It does this by monitoring and adjusting the control parameters (such as temperature or flow rate) to ensure that the output product remains as it should.Figure 7 A closed-loop oven-heating system.The classic example of process control is a

Process Control - Sequentially Controlled Systems

2008-04-22T17:30:00.000-07:00

A sequentially controlled system controls a process that is defined as a series of tasks to be performed—that is, a sequence of operations, one after the other. Each operation in the sequence is performed either for a certain amount of time, in which case it is time-driven, or until the task is finished (as indicated by, say, a limit switch), in which case it is event-driven. A time-driven

CLASSIFICATIONS OF CONTROL SYSTEMS - Motion Control

2008-04-22T17:29:00.002-07:00

Motion control is a broad term used to describe an open-loop or closed-loop electromechanical system wherein things are moving. Such a system typically includes a motor, mechanical parts that move, and (in many cases) feedback sensor(s). Automatic assembling machines, industrial robots, and numerical control machines are examples.ServomechanismsServomechanism is the traditional term applied to

Introduction to Microprocessor-Based Control

2008-04-22T17:29:00.001-07:00

The digital integrated circuit (IC) called a microprocessor [Figure 1(a)], has ushered in a whole new era for control systems electronics. This revolution has occurred because the microprocessor brings the flexibility of program control and the computational power of a computer to bear on any problem. Automatic control applications are particularly well suited to take advantage of this

INTRODUCTION TO MICROPROCESSOR SYSTEM HARDWARE

2008-04-22T17:28:00.001-07:00

A computer is made up of four basic functional units: the central processing unit (CPU), memory, input, and output (I/O). The central processing unit does the actual computing and is composed of two subparts: the arithmetic logic unit and control sections Figure 2. The arithmetic logic unit (ALU) performs the actual numerical and logic calculations such as addition, subtraction, AND, OR, and

INTRODUCTION TO MICROPROCESSOR OPERATION

2008-04-22T17:27:00.000-07:00

The microprocessor works by executing a program of instructions. Creating the program is similar in concept to programming in BASIC, C, or any other high-level computerlanguage. Each type of microprocessor has its own instruction set, which is the set of commands that it was designed to recognize and obey. Microprocessor instructions are very elemental and specific, and it usually takes more than

INTERFACING TO A MICROPROCESSOR CONTROLLER - The Parallel Interface

2008-04-22T17:26:00.000-07:00

The parallel interface transfers data 8 bits (or more) at the same time, using eight separate wires. It is essentially an extension of the data bus into the outside world. The parallel interface is ideal for inputting or outputting data from devices that are either on or off. For example, a single limit switch uses only one input bit, and an on-off signal to a motor requires only one output bit.

INTERFACING TO A MICROPROCESSOR CONTROLLER - The Serial Interface

2008-04-22T17:25:00.000-07:00

In a serial interface, the data are sent 1 bit after the other on a single wire. There are a number of good reasons for doing this. First, the cabling is simpler because only two wires are needed (at a minimum), those being “data” and “return.” Second, shielding a small group of wires, which is often necessary in an electrically noisy industrial environment, is easier. Third, serial data can make

INTRODUCTION TO CONTROLLER PROGRAMMING

2008-04-22T17:24:00.001-07:00

It is beyond the scope of this text to present a detailed discussion of how to program a microprocessor in machine language. Still,it is useful to investigate in a general way what the software must do. A digital controller is a computer operating in real time.This means that the program is running all the time—repeatedly taking in the newest sensor data and then calculating a new output for the