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E-mail: ured@znanost.hr

**Type of paper**:*Paper in journal*

**Title:**Modeling of Dynamics and Control Systems design of Two-Phase Circulation System of Steam Generator

**Authors:**- Kecman, Vojislav (20623)
- Petrić, Joško
- Majetić, Dubravko (162406)
- Široki, Mladen (162364)

**Journal**: Strojarstvo

**Number**: 2

**ISSN**: 0562-1887

**Volume**: 33

**Year**: 1991

**Pages**: from 117 to 124

**Number of references**: 16

**Language**: hrvatski

**Summary**: The paper cinsiders the development of teh mathematical model ofsteam generator circulation loop dynamics, the linearization oforiginal model and design of modern multivariable control system.The limitations and benefits of linear control theory arediscused. Based on the results of simulations of this linearmodel it is posibble to evaluate the importance of measurement ofparticular state variables and to test the integrity of controlsystem. Particular attention is given to the implementation oflinear optimal controller considering the real nonlinearenvironment consisting of bounded controlling elements. Theinfluence of the set-points changes, feedback control matrixgains and manipulating elements to the response of nonlinearsystem is analysed.

**Keywords**: Mathematical modeling, Nonlinear dynamics, Optimal controller, Steam generator

**Type of paper**:*Paper in proceedings*

**Title:**Synthesis of PID Controller by Neural Network

**Authors:**- Majetić, Dubravko (162406)
- Kecman, Vojislav (20623)

**Editors**- Kecman, Vojislav (20623)

**Proceedings title**: Zbornik Radova KoREMA'36

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1991

**ISBN/ISSN**: 86-81571-09-5

**Pages**: from 55 to 58

**Meeting**: Sedmi simpozij o sistemima automatskog upravljanja

**Held**: from 04/18/91 to 04/20/91

**Summary**: A paper presents synthesis of PID controller byHetero-associative, four-layer, back-propagation neural network.One of the possible application of neural networks in the fieldof automatic control is discussed.

**Keywords**: Neural network, Error back-propagation, PID controller

**Type of paper**:*Paper in proceedings*

**Title:**Neural Network as PID Controller

**Authors:**- Majetić, Dubravko (162406)

**Editors**- Srb, Neven

**Proceedings title**: Zbornik radova 3.SONT

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1991

**ISBN/ISSN**: 86-81997-02-5

**Pages**: from 62 to 65

**Meeting**: Četvrti međunarodni simpozij o novim tehnologijama

**Held**: from 10/15/91 to 10/17/91

**Summary**: The learning procedure of Hetero-associative, four-layer,back-propagation neural network for learning the PID algorithm ispresented. One of the possible applications of neural network inthe field of automatic control is discussed.

**Keywords**: Neural network, PID Controller, Learning procedure, Recall procedure

**Type of paper**:*Paper in proceedings*

**Title:**Neural Network as Ideal Controller In Open-Loop Control

**Authors:**- Majetić, Dubravko (162406)
- Vinković, Mladen

**Editors**- Novaković, Branko (33541)

**Proceedings title**: Zbornik radova KoREMA'38

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1993

**ISBN/ISSN**: 953-6037-00-9

**Pages**: from 450 to 454

**Meeting**: 38. Međunarodni godišnji skup KoREMA : Sekcija Automatsko upravljanje

**Held**: from 04/26/93 to 04/28/93

**Summary**: As neural networks shows very good results during the learningand recall process od dinamics behaviours with distinctderivation properties, which are the main characteristic of idealcontrollers, the possible application of neural network in theconcept of ideal controller is discussed.

**Keywords**: Neural network, Inverse-dynamics Controller, Open-Loop Controll

**Type of paper**:*Paper in proceedings*

**Title:**Simulation of Dinamic Elements by Neural Network

**Authors:**- Majetić, Dubravko (162406)

**Editors**- Kecman, Vojislav (20623)

**Proceedings title**: Zbornik radova KoREMA'37

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1992

**ISBN/ISSN**: 86-81571-11-7

**Pages**: from 46 to 49

**Meeting**: 37. Međunarodni godišnji skup KoREMA - 8. Simpozij o sistemima automatskog upravljanja

**Held**: from 04/26/92 to 04/29/92

**Summary**: In this paper an approach to the learning procedure of errorback-propagation neural network for learning some basic dynamicbehaviours such as P1, PD, I0 and P2 is presented. The number ofprocessing elements in input and output layer is defined bydicrete form of dynamic element. Consideration is given tocertain problems wich are connected with discrete form of dynamicelement and which occurs in recall procedure of trained neuralnetwork.

**Keywords**: Neural network, Error back-propagation, dicrete form, learning procedure, recall procedure

**Type of paper**:*Paper in proceedings*

**Title:**Neural Network Image Processing Based on Textural Features

**Authors:**- Široki, Mladen (162364)

**Editors**- Kecman, Vojislav (20623)

**Proceedings title**: Zbornik radova KoREMA'37

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1992

**ISBN/ISSN**: 86-81571-11-7

**Pages**: from 60 to 64

**Meeting**: 37. Međunarodni godišnji skup KoREMA - 8. Simpozij o sistemima automatskog upravljanja

**Held**: from 04/26/92 to 04/29/92

**Summary**: Neural network was used in recognition three different groups ofobjects with similar geometrical shape, but different texture.They included mushrooms, biscuits and pieces of rounded paper.Recognition is based on textural features extracted fromco-occurrence matrices. The overall identification accuracy wasmore than 99%.

**Keywords**: Neural network, Back-Error propagation, Co-occurrence matrices, Textural features

**Type of paper**:*Paper in proceedings*

**Title:**On the Realation Between the Cost Function and the Output-Layer Neurons Activation Function

**Authors:**- Kecman, Vojislav (20623)

**Proceedings title**: Procceedings of '15. Kolloquium der Automatisierungstechnik

**Language**: engleski

**Place**: Bremen, Njemačka

**Year**: 1993

**Pages**: from 0 to 0

**Meeting**: 15' Koloquium der Automatisierunhstechnik

**Summary**: This paper examines the relation between the cost function andthe activation function (AF) of the output-layer neurons ofneural networks. It is important to realize that the well formedcost (or error) function, when training artificial neuralnetworks (ANN) using Error Back Propagation algorithm, cangreatly reduce the learning time. This cost function isdetermined by final task the ANN is applied for i.e., by probabilistic model for how the training data are being generated. At the same time, the type of AF of the output-layer neurons is not independent but rather connected with the type ofthe very cost function used.

**Keywords**: Cost (error) function, Activation function, Neural Networks

**Type of paper**:*Paper in proceedings*

**Title:**EBP Work With Hard Limiters

**Authors:**- Kecman, Vojislav (20623)

**Proceedings title**: Proceedings of International Conference on Artificial Neural Networks

**Language**: engleski

**Place**: Amsterdam, Nizozemska

**Year**: 1993

**Pages**: from 0 to 0

**Meeting**: International Conference on Artificial Neural Networks

**Type of paper**:*Paper in proceedings*

**Title:**Dynamic Properties and Control of the Flexible Manipulators

**Authors:**- Petrić, Joško
- Kecman, Vojislav (20623)

**Editors**- Cebalo, Roko

**Proceedings title**: Zbornik radova Suvremeni trendovi proizvodnog strojarstva

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1992

**Pages**: from 143 to 151

**Meeting**: Suvremeni trendovi proizvodnog strojarstva

**Held**: from 05/09/92 to 05/09/92

**Summary**: Flexible manipulators become one of the most interesting field ofrobotics today. In this paper some basic dificulties, phenomenaand issues connected by the approach to the manipulators aselastic bodies are considered. Mention is also made about thequestion - is it necessary to control the motion of themanipulators including flexibility of their links into thefeedback?!

**Keywords**: Flexible Manipulator, Feedback Control, State-Space Control

**Type of paper**:*Paper in proceedings*

**Title:**Nonminimum-Phase Property of the Elastic Mechanical Structures

**Authors:**- Petrić, Joško

**Editors**- Crnošija, Petar

**Proceedings title**: Zbornik radova KoREMA'93

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1993

**ISBN/ISSN**: 953-6037-00-9

**Pages**: from 715 to 719

**Meeting**: 38. Međunarodni godišnji skup KoREMA : Sekcija Automatsko upravljanje

**Held**: from 04/26/93 to 04/28/93

**Summary**: In this paper nonminimum-phase property of the noncolocatedcontrolled elastic mechanical structures is demonstrated on anexample of a single-link flexible manipulator. This property,absent in the classical control approach of rigid manipulators,is cause of the detoriated capability of path following andprecise positioning of the flexible manipulators. Therefore,herein the attention will be given on the characteristics of themanipulators depending on the actuator and sensor location alongthe link, and some connected control problems will be alsoconsidered.

**Keywords**: Nonminimum-Phase Property, Elastic Body, Flexible Manipulator

**Type of paper**:*Paper in proceedings*

**Title:**On the Control of the Flexible Manipulator Arm

**Authors:**- Petrić, Joško
- Kecman, Vojislav (20623)

**Proceedings title**: IFAC Symposium on Robot Control - SYROCO'91

**Language**: engleski

**Place**: Wien, Austrija

**Year**: 1991

**Pages**: from 325 to 330

**Meeting**: IFAC Symposium on Robot Control

**Held**: from 09/16/91 to 09/18/91

**Summary**: The paper considers the control of a class of robots whose armflexibility, due to light construction and the demand for fastrobot motion, is not negligible. The mathematical model ofdynamics of one translational joint and one flexible armmanipulator with the payload on its end is developed. On thebasis of the mathematical model developed, an optimal linearquadratic state regulator is designed. In the paper the influenceof additional control action, using the states of vibration modesof the manipulator arm, is analysed.

**Keywords**: Robot, Flexible Manipulator, Modeling, State Space, Optimal Control

**Type of paper**:*Paper in proceedings*

**Title:**State-Space Based Control System Design for Flexible Manipulators

**Authors:**- Kecman, Vojislav (20623)
- Petrić, Joško
- Petrić, Joško

**Editors**- Cebalo, Roko

**Proceedings title**: Fachtagugn Automatisierung Dresden'92

**Language**: engleski

**Place**: Dresden, Njemačka

**Year**: 1992

**Pages**: from 146 to 152

**Meeting**: Fachtagung Automatisierung

**Held**: from 02/20/92 to 02/21/92

**Summary**: The paper considers the joint-states feedback controland the control which also involves feedback of vibration statesin the flexible single link manipulator case. An optimal linearquadratic state controller is applied and the advances in thecase of extended, noncolocated feedback control are discussed.

**Keywords**: Flexible Manipulator, State-Space, Optimal LQ Controller

**Type of paper**:*Paper in proceedings*

**Title:**Applicatin of Artificial Neural Networks in Robotics

**Authors:**- Novaković, Branko (33541)

**Editors**- Cebalo, Roko

**Proceedings title**: Zbornik radova CIM'93

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1993

**Pages**: from 1 to 8

**Meeting**: 2. Međunarodno savjetovanje proizvodnog strojarstva

**Held**: from 11/18/93 to 11/18/93

**Summary**: Starting with the general artificial neural network model,described as discrete time nonlinear system, the feedforwardneural network for robot control has been derived. This networkis trained to imitate an adaptive nonlinear robot controlalgorithm based on the dynamics of the full robot model of RRTR-structure. As the result we have the neural network , which cancompute both the nominal and feedback robot control, using theparallel processing.

**Keywords**: Artificial neural networks, idustrial robot control, adaptive control, fast learning algorithms.

**Type of paper**:*Paper in proceedings*

**Title:**Neural Networks and Control in Robotics and FMS : Conceptual overview

**Authors:**- Novaković, Branko (33541)
- Lišćić, Igor
- Furač, Zvonimir

**Editors**- Srb, Neven

**Proceedings title**: Zbornik radova 4.SONT

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1993

**ISBN/ISSN**: 86-81997

**Pages**: from 31 to 34

**Meeting**: Četvrti međunarodni simpozij o novim tehnologijama

**Held**: from 10/25/93 to 10/27/93

**Summary**: A conceptual overview of the state of the art in theory andapplications of neural networks in automatic control and roboticsis given in this paper. Besides, some new methods are proposed.The control methods of flexible manufacturing cell are discussedat the end of this paper.

**Keywords**: Neural networks, industrial robots, flexible manufacturing systems, a conceptual overview.

**Type of paper**:*Paper in proceedings*

**Title:**An Application of Neural Networks in the Control of the Industrial Robots

**Authors:**- Lišćić, Igor
- Novaković, Branko (33541)
- Furač, Zvonimir

**Editors**- Novaković, Branko (33541)

**Proceedings title**: Zbornik radova KoREMA'38

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1993

**ISBN/ISSN**: 953-6037-00-9

**Pages**: from 458 to 461

**Meeting**: 38. Međunarodni godišnji skup KoREMA : Sekcija Automatsko upravljanje

**Held**: from 04/26/93 to 04/28/93

**Summary**: This paper presents the realisation of one link control ofindustrial robot, using artificial neural networks (ANN). Thecontrol system is created by two ANN, where the first one computsnominal control, and the second network is a nonlinear controllerof position and velocity of robot.

**Keywords**: Artificial neural networks, nominal control, controller of position and velocity of robot.

**Type of paper**:*Paper in proceedings*

**Title:**Aquisition Software Possibilities for Vibration Measurement

**Authors:**- Grilec, Josip (14562)

**Editors**- Šurina, Tugomir

**Proceedings title**: Zbornik radova BIAM'92

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1993

**ISBN/ISSN**: 86-81571-12-5

**Pages**: from 44 to 49

**Meeting**: XI Međunarodno savjetovanje BIAM'92

**Held**: from 06/16/92 to 06/17/92

**Summary**: This article gives the possibilities of the programme which notonly supports the sampling circuits, but can also quickly performa series of activities concerning processing, graphicalpresentation and storing of vibration signals on a personalcomputer.

**Keywords**: Acquisition software, vibration measurement, signal sampling.

**Type of paper**:*Paper in proceedings*

**Title:**Criterion Systematisation for Sensor Selection in Flexible Manufacturing System

**Authors:**- Grilec, Josip (14562)

**Editors**- Srb, Neven

**Proceedings title**: Zbornik radova 3.SONT

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1991

**ISBN/ISSN**: 86-81997-02-5

**Pages**: from 66 to 68

**Meeting**: Treći međunarodni simpozij o novim tehnologijama

**Held**: from 10/15/91 to 10/17/91

**Summary**: This paper discusses procedures for choosing sensors of flexiblemanufacturing systems and a systematisation of characteristicfeatures with restrictions. As one has to be aware of somepossible environmental influences, a number of desirablecharacteristic features according to different criterions arepresented. Because of the poor standards special stress is put onthe importance of the right choice of sensors.

**Keywords**: Criterion systematisation, environmental influences, flxible manufacturing systems, sensor selection.

**Type of paper**:*Paper in proceedings*

**Title:**Application of Parallel Processing on Robot Dynamic Control

**Authors:**- Zorc, Davor (111286)

**Editors**- Srb, Neven

**Proceedings title**: Zbornik radova 3.SONT

**Language**: hrvatski

**Place**: Opatija

**Year**: 1991

**ISBN/ISSN**: 86-81997-02-5

**Pages**: from 83 to 87

**Meeting**: Treći međunarodni simpozij o novim tehnologijama

**Held**: from 10/15/91 to 10/17/91

**Keywords**: Parallel Processing, Process Alocation, Robot Control

**Type of paper**:*Paper in proceedings*

**Title:**Exploiting The Structural Equivalence of Leraning Fuzzy Systems and Radial Basis Function Neural Networks

**Authors:**- Kecman, Vojislav (20623)
- Pfeifer, Bernard-Markus

**Proceedings title**: Proceedings of Sec.Eur. Congress on Intell. Techniq. and Soft Comp., EUFIT

**Language**: engleski

**Place**: Aachen, Njemačka

**Year**: 1994

**Pages**: from 58 to 66

**Meeting**: Seccond Europian Congress on Intell. Techniq. and Soft Comp., EUFIT'94

**Summary**: This paper shows when and how the Learning of Fuzzy Rules (LFR) from numerical data is equal to the training of Radial Basis Function (RBF) or regularization networks. Although both approaches have their origin in different paradigms of "intellignet" information processing, it is demonstrated that the mathematical structure is the same and they share the property of being "universal approximators" of any real coninuous function on a compact set to arbitrary accuracy. In the LRF algorithm proposed here the subject of learning are the rule conclusions, i.e. the membership functions of output attributes in the form of singletons. For fixed number, location and shape of input membership functions LFR and RBF training are least squares optimization problems linear in the unknown parameters, and in this case the solution boils down to the pseudo inversion of one rectangular matrix. As an application example the dynamic identification of a nonlinear real world system from noisy measurement data will be performed with LRF and with "Soft" RBF.

**Keywords**: neuro fuzzy systems, fuzzy identification, RBF networks

**Type of paper**:*Paper in proceedings*

**Title:**Dynamic Neurla Network for Adaptive Nonlinear Robot Control

**Authors:**- Majetić, Dubravko (162406)

**Proceedings title**: Zbornik radova 40. jubilarni godišnji skup KoREMA

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1995

**ISBN/ISSN**: 953-6037-08-4

**Pages**: from 498 to 501

**Meeting**: 40. jubilarni godišnji skup KoREMA

**Held**: from 04/19/95 to 04/21/95

**Summary**: The paper presents a possibility of application of a multilayer feedforward dynamic neural network in robot control. The hidden layer consists of dynamic neurons. Eash dynamic neuron posseses one dynamic element with two poles and two zeros followed by hyperbolic tangent activation function. The output layer consists of static neurons with linear activation function. This neural network is trained to imitate an adaptive nonlinear robot control algorithm, based on inerse dynamics of the full robot model of RRTR-structure. For that purpose the error backpropabation supervised learning algorithm with stochastic learnig is developed and neurons weights and coefficients of dynamic neurons are learned simultaneously.

**Keywords**: dynamic neural network, adaptive nonlinear robot control

**Type of paper**:*Paper in proceedings*

**Title:**Eksperimental Model of the Flexible Robot

**Authors:**- Petrić, Joško
- Deur, Joško

**Proceedings title**: Zbornik radova 12. međunarodnog savjetovanja BIAM'94

**Language**: hrvatski

**Place**: Zagreb

**Year**: 1994

**Pages**: from F15 to F18

**Summary**: In this paper an experimental model of the robot with one rotation joint and a very flexible link is presented. A mathematical model of robot dynamics is verified, and also experiments on the flexible robot control was done on this laboratory facility. A brief research description is given in the paper. Significance and reasons of research on the field of flexible robots, from authors point of view, are also explained here.

**Keywords**: flexible robot, eksperimental model, control

**Type of paper**:*Paper in proceedings*

**Title:**State of the Art in Neural Networks Applications to Robot Control

**Authors:**- Novaković, Branko (33541)

**Editors**- Novaković, Branko (33541)

**Proceedings title**: Zbornik radova 40.Jubilarni godišnji skup KoREMA

**Language**: engleski

**Place**: Zagreb

**Year**: 1995

**ISBN/ISSN**: 953-6037-08-4

**Pages**: from 487 to 491

**Meeting**: 40. jubilarni godišnji skup KoREMA

**Held**: from 04/19/95 to 04/21/95

**Summary**: In the paper an overview of the applications of feedforward, feedback, and adaptive Neural Networks (NN's) to robot control is presented. The following items are pointed out :(i) feedforward NN for adaptive nonlinear robot control, (ii) a neurla network for multi-manipulator system control, (iii) a nonnumerical environment modelling, (iii) adaptive NN for adaptive nonlinear robot control, (iiii) NN controllers for mobile robots, (iiiii) a global linarising control using recurrent NN, and (iiiiii) making of self-robot control more "human-like".

**Type of paper**:*M.A.*

**Title:**Contribution to the Use Neural Networks in the Systems of Automatic Control

**Authors:**- Kecman, Vojislav (20623)
- Majetić, Dubravko (162406)

**Faculty**: Strojarstva i brodogradnje Zagreb

**Date of defense**: 02/14/92

**Language**: hrvatski

**Number of pages**: 130

**Summary**: In the M.S. thesis, entilted "THE CONTRIBUTION TO THE USE NEURALNETWORKS IN THE SYSTEMS OF AUTOMATIC CONTROL" the learningprocedure of hetero-associative, multilayer, backpropagationneural network for learning some basic dynamic behaviours such asP1, P2, D1, PD and I0 is presented. The analysis of the structure of proposed neural network is alsogiven, with special emphasis on the number of hidden layers,number of processing elements in output, hidden and input layer,and on the content of learning file. The optimal setting of learning coefficients C1 and C2 inlearning rule during the learning procedure is proposed. The processing element transfer function analysis has also beencarried out and in the sense of faster decreasing of learningerror the best processing element transfer function is proposed. Possible applications of neural networks in the field ofautomatic control is also discussed. Consideration is given tocertain other questions which may be resolved by further researchand which, at the same time, set the guidelines for continuedresearch in this area.

**Keywords**: Neural networks, Number of hidden layers, Number of Neurons, Activation Function, Learning Coefficients

**Type of paper**:*M.A.*

**Title:**Contribution to the Development of Inteligent Vision System

**Authors:**- Kecman, Vojislav (20623)

**Faculty**: Strojarstva i brodogradnje Zagreb

**Date of defense**: 11/19/92

**Language**: hrvatski

**Number of pages**: 143

**Summary**: In this report one approach of the Automatic Intelligent VisionSystem will be presented. The Vision System described in thiswork consists of the following stages : image capture, low levelimage processing, features extraction and objects recognition.The special attention in this work is given to the featuresextraction and objects recognition by the Back-Error propagationneural network. The objects recognition is based on the texturalfeatures extracted from Gray-Tone Spatial Dependence matrices(Co-occurrence matrices). The most significant textural featureswere selected by Stepwise discriminant analysis (Wilks method).Objects were classified in their groups by Back-Error propagationneural network. The transputer based Vision system is designed and developed. Thetransputers offer new possibilities in designing and programmingcost-effective concurrent system. It is possible to build aconcurrent system which will perform a real-time imageprocessing. The Vision system is used in classifying three different groupsof objects with similar geometrical shapes but different surfacetexture. They included mushrooms, biscuits and pieces of roundedpaper. The objects were divided in two sets - training set andtest set. The number of textural features were extracted fromCo-occurrence matrices of objects from training set. The mostsignificant features were selected by Stepwise discriminantanalysis. These features were used for the Back-Error propagationneural network training. The network were trained until the errorfor entire set was less then 0.01. The Vision System was testedby the test set. The overall identification accuracy produced wasmore then 99 % . These results indicate that the neural networkclassification based on the textural features extracted fromCo-occurrence matrices can be very promising method, and probablycan find wide applications in the practice.

**Keywords**: Vision System, Neural Network, Back-Error propagation, Co-ocurrence matrices, Textural features

**Type of paper**:*Mentorship*

**Title:**Contribution to the Use Neural Networks in the Systems of Automatic Control

**Faculty**: Strojarstva i brodogradnje Zagreb

**Mentor**: ADLER NEVENKA

**Date of defense**: 02/14/92

**Number of pages**: 130

**Author**: Majetić mr.sc. Dubravko

**Degree level**: M.A.

**Type of paper**:*Mentorship*

**Title:**Contribution to the Development of Inteligent Vision System

**Faculty**: Strojarstva i brodogradnje Zagreb

**Mentor**: ADLER NEVENKA

**Date of defense**: 11/19/92

**Number of pages**: 143

**Author**: Široki mr.sc. Mladen

**Degree level**: M.A.

**Type of paper**:*Survey/Study*

**Title:**The Automatic Harvesting of the Mushrooms - the Grasping Mechanism

**Authors:**- Široki, Mladen (162364)

**Ordering party**: The University of Wollongong, Department of Electrical and Computer Engineering

**Institution depot**: The University of Wollongong, Department of Electrical and Computer Engineering

**Year**: 1991

**Number of pages**: 23

**Language**: engleski

**Summary**: In this report the design, development and implementation of amushroom grasping mechanism will be presented. This is the partof the Automatic Harvesting of the Mushrooms project, which aimsto design and develop Automatic Mushroom Harvesting which willbe able to singularise, recognize, grasp and harvest themushrooms without demaging them.

**Keywords**: Grasping Mechanism, Mushroom harvesting, Automatic Mushroom Harvesting Machine

**Type of paper**:*Survey/Study*

**Title:**The Automatic Harvesting of the Mushrooms - the Vision System

**Authors:**- Široki, Mladen (162364)

**Ordering party**: The University of Wollongong, Department of Electrical and Computer Engineering

**Institution depot**: The University of Wollongong, Department of Electrical and Computer Engineering

**Year**: 1992

**Number of pages**: 20

**Language**: engleski

**Summary**: In this report using of textural features extracted fromCo-occurrence matrices will be presented. The method is intendedto be used in an automatic Mushroom Harvesting Machine.Classification is performed by Back-Error Propagation NeuralNetwork.

**Keywords**: Vision system, Textural features, Co-occurrence matrices, Neural Network, Back-error propagation

**Type of paper**:*Survey/Study*

**Title:**Application of Artificial Neural Networks for Identification of System Dynamics

**Authors:**- Kecman, Vojislav (20623)

**Ordering party**: Massachusetts Institue of Tecmnology, Department of Mechanical Engineering

**Institution depot**: Massachusetts Institue of Tecmnology, Department of Mechanical Engineering

**Year**: 1993

**Number of pages**: 74

**Language**: engleski

**Summary**: This report discusses the application of Feed-Forward Multy-LayerArtificial Neural Networks in the field of system dynamicsidentification. ANNs are presented to be general approximators ofmultivariate functions. In this fremwork, identification is doneusing mathematical representation of system dynamics as mappingof time histories of past (and current) input and past outputsignals of dynamic system into a current output signal. The imactof the number of hidden layers, number of neurons in them, typeof activation function in neurons and training function onaccuracy and generalization capabilities of ANN model of dinamicsystem is analyzed. Basic of Regularization (Radial BasisFunction) Networks are introduced as well as the survey ofcurrent relevant literature from the field is given.

**Keywords**: Neural networks, Identification, System Dynamics Modeling

**Type of paper**:*Survey/Study*

**Title:**Approximation Capabilities of ANNs as Function of the Number of Hidden Layer Neurons and the Type of Activation Function

**Authors:**- Kecman, Vojislav (20623)
- Majetić, Dubravko (162406)

**Ordering party**: Massachusetts Institue of Tecmnology, Department of Mechanical Engineering

**Institution depot**: Massachusetts Institue of Tecmnology, Department of Mechanical Engineering

**Year**: 1993

**Number of pages**: 19

**Language**: engleski

**Summary**: This paper discusses the application of Feed-Forward Multy-LayerArtificial Neural Networks in the field of system dynamicsidentification. ANNs are presented to be general approximators ofmultivariate functions. Identification is done using mathematicalrepresentation of system dynamics as mapping of time histories ofpast (and current) input and past output signals of dynamicsystem into a current output signal. The imact of the number ofhidden layers, number of neurons in them, type of activationfunction in neurons and training function on accuracy andgeneralization capabilities of ANN model of dinamic system isanalyzed.

**Keywords**: Approximation capabilities, Hidden layer, Activation function

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