Nenad T. Pavlović

Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties

Highlights? Adaptive neuro-fuzzy estimation of conductive silicone rubber properties. ? Adaptive neuro-fuzzy network to approximate correlation between measured features of the material. ? Adaptive neuro-fuzzy network to predict the conductive silicone rubber future behavior for stress changing. ? A new constitutive model of the conductive silicone rubber. ? A new type of stress prediction model based on artificial neural network. Conductive silicone rubber has great advantages for tactile sensing applications. The electrical behavior of the elastomeric material is rate-dependent and exhibit hysteresis upon cyclic loading. Several constitutive models were developed for mechanical simulation of this material upon loading and unloading. One of the successful approaches to model the time-dependent behavior of elastomers is Bergstrom-Boyce model. An adaptive neuro-fuzzy inference system (ANFIS) model will be established in this study to predict the stress-strain changing of conductive silicone rubber during compression tests. Various compression tests were performed on the produced specimens. An ANFIS is used to approximate correlation between measured features of the material and to predict its unknown future behavior for stress changing. ANFIS has unlimited approximation power to match any nonlinear functions well and to predict a chaotic time series.

Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces

It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult t@? analyze with conventional analytical methods. Here, a novel design of an adaptive neuro fuzzy inference system (ANFIS) for estimation contact forces of a new adaptive gripper is presented. Since the conventional analytical methods is a very challenging task, fuzzy logic based systems are considered as potential candidates for such an application. The main points of this paper are in explanation of kinetostatic analyzing of the new gripper structure using rigid body model with added compliance in every single joint. The experimental results can be used as training data for ANFIS network for estimation of gripping forces. An adaptive neuro-fuzzy network is used to approximate correlation between contact point locations and contact forces magnitudes. The simulation results presented in this paper show the effectiveness of the developed method. This system is capable to find any change in ratio of positions of the gripper contacts and magnitudes of the contact forces and thus indicates state of both finger phalanges.

Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect

We present a project management methodology designed for the selection of wind turbines wake effect most influential parameters, who need to run wind farm project for large energy conversion. Very frequently, the managers of these projects are not project management professionals, so they need guidance to have autonomy, using minimal time and documentation resources. Therefore, agile method is adapted to assist the project management. Wind energy poses challenges such as the reduction in the wind speed due to the wake effect by other turbines. If a turbine is within the area of turbulence caused by another turbine, or the area behind another turbine, the wind speed suffers a reduction and, therefore, there is a decrease in the production of electricity. In order to increase the efficiency of a wind farm, analyzing the parameters, which have influence on the wake effect, is one of the focal research areas. To maximize the power produced in a wind farm, it is important to determine and analyze the most influential factors on the wake effects or wake wind speeds since the effect has most influence on the produced power. This procedure is typically called variable selection, and it corresponds to finding a subset of the full set of recorded variables that exhibits good predictive abilities. In this study, architecture for modeling complex systems in function approximation and regression was used, based on using adaptive neuro-fuzzy inference system (ANFIS). Variable searching using the ANFIS network was performed to determine how the five parameters affect the wake wind speed. Our article answers the call for renewing the theoretical bases of wind farm project management in order to overcome the problems that stem from the application of methods based on decision-rationality norms, which bracket the complexity of action and interactions in projects.

Support vector machine firefly algorithm based optimization of lens system

Lens system design is an important factor in image quality. The main aspect of the lens system design methodology is the optimization procedure. Since optimization is a complex, nonlinear task, soft computing optimization algorithms can be used. There are many tools that can be employed to measure optical performance, but the spot diagram is the most useful. The spot diagram gives an indication of the image of a point object. In this paper, the spot size radius is considered an optimization criterion. Intelligent soft computing scheme support vector machines (SVMs) coupled with the firefly algorithm (FFA) are implemented. The performance of the proposed estimators is confirmed with the simulation results. The result of the proposed SVM-FFA model has been compared with support vector regression (SVR), artificial neural networks, and generic programming methods. The results show that the SVM-FFA model performs more accurately than the other methodologies. Therefore, SVM-FFA can be used as an efficient soft computing technique in the optimization of lens system designs.

Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology

The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Adaptive Neuro-Fuzzy Evaluation of the Tapered Plastic Multimode Fiber Based Sensor Performance with and Without Silver Thin Film for Different Concentrations of Calcium Hypochlorite

An adaptive neurofuzzy (ANFIS) evaluation study has been applied on tapered plastic multimode sensors. This study is basically using tapered plastic multimode fiber polymethyl methacrylate (PMMA) optical as a sensor. It is proposed and demonstrated for continuous monitoring of solutions based on different concentration of sodium chloride and glucose in deionized water. The tapered PMMA fiber was fabricated using an etching method involving deionized water and acetone to achieve a waist diameter and length of 0.45 and 10 mm, respectively. In addition, a tapered PMMA probe, which was coated by silver film, was fabricated and demonstrated using calcium hypochlorite (G70) solution. The working mechanism of such a device is based on the observation increment in the transmission of the sensor that is immersed in solutions at higher concentration. As the concentration varies from 0 to 6 ppm, the output voltage of the sensor increases linearly from 3.61 to 4.28 mV with a sensitivity of 0.1154 mV/ppm and a linearity of more than 99.47%. The silver film coating increases the sensitivity of the proposed sensor due to the effective cladding refractive index, which increases with the coating and thus allows more light to be transmitted from the tapered fiber. To estimate the output voltage response of the sensors with and without silver film, this paper constructed a process, which simulates the sensors voltage output in regard to different concentration of calcium hypochlorite with ANFIS method. This intelligent estimator is implemented using MATLAB/SIMULINK and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Vibration prediction of pellet mills power transmission by artificial neural network

Purpose The purpose of this study is to establish a vibration prediction of pellet mills power transmission by artificial neural network. Vibration monitoring is an important task for any system to ensure safe operations. Improvement of control strategies is crucial for the vibration monitoring. Design/methodology/approach As predictive control is one of the options for the vibration monitoring in this paper, the predictive model for vibration monitoring was created. Findings Although the achieved prediction results were acceptable, there is need for more work to apply and test these results in real environment. Originality/value Artificial neural network (ANN) was implemented as the predictive model while extreme learning machine (ELM) and back propagation (BP) learning schemes were used as training algorithms for the ANN. BP learning algorithm minimizes the error function by using the gradient descent method. ELM training algorithm is based on selecting of the input weights randomly of the ANN network and the output weight of the network are determined analytically.