Browsing by Author "Tuntas, R."

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The Modeling and Hardware Implementation of Semiconductor Circuit Elements by Using Ann and Fpga
(Polish Acad Sciences inst Physics, 2015) Tuntas, R.
This study, the modeling and hardware implementation of semiconductor circuit elements very frequently used in electronic circuits are carried out by using artificial neural networks and field programmable gate array chip. Initially the artificial neural network models obtained has been written in very high speed integrated circuit hardware description language (VHDL). Then, these configurations have been simulated and tested under ModelSim Xilinx software. Finally, the best configuration has been implemented under the Xilinx Spartan-3E FPGA (XC3S500E) chip of Xilinx. The modeling of electronic circuit elements is very important both in respect of engineering, and in respect of practical mathematics. The main aim is to shorten the simulation time and to examine the real physical system applications easily by using the model elements instead of using the ones used in real applications. The effectiveness of the implemented artificial neural network models on field programmable gate array was found successful.
Predict and Analysis of the Full-Wave Rectifier Circuit With Fuzzy Modeling Technique Based on Performances of Different Membership Functions
(2012) Tuntas, R.
In the present study, A neuro-fuzzy modelling technique was used to predict and simulation the full-wave rectifier circuit. Structure of the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) was improved and trained in MATLAB toolbox. A hybrid learning algorithm consists of back-propagation and least-squares, and the real hardware data were used for training the ANFIS network. Efficiency of the developed ANFIS modelling techniques with different membership functions (MFs) was tested and a mean 99.999% recognition success was obtained. Furthermore, the full-wave rectifier circuit was simulated with the HSPICE simulation program for testing the simulation speed of ANFIS and HSPICE. The comparison between ANFIS and HSPICE indicates the feasibility and accuracy of the proposed neuro-fuzzy modelling technique. The results showed that the proposed ANFIS simulation has much higher speed and accuracy in comparison with HSPICE simulation. The neuro-fuzzy modelling technique can be simply used in software tools for designing and simulation of the full-wave rectifier circuit and the other electronic circuit. © Sila Science.
The Modelling and Analysis of Nonlinear Systems Using a New Expert System Approach
(Springer international Publishing Ag, 2014) Tuntas, R.
In the present study, a new modelling technique was developed for the modelling and analysis of hyperchaotic systems using an expert system based on wavelet decompositions and the Adaptive Neuro-Fuzzy Inference System (ANFIS). The success and superior properties of this new expert system were shown by applying the hyperchaotic Chen system which is a hyperchaotic system. The obtained expert system consists of two layers, including wavelet decomposition and ANFIS. Wavelet decomposition was used for extracting features in the first layer, and ANFIS was used for system modelling in second layer. Furthermore, HSPICE simulation of the hyperchaotic Chen system was carried out for comparison with the proposed expert system. The structure of the ANFIS was improved and trained in the MATLAB toolbox. Numerical simulations were used in this study. Five various data sets have been used to test the simulation speed of the proposed expert system and HSPICE. The obtained results show that the proposed expert system simulation has much higher speed and accuracy in comparison with HSPICE simulation. The proposed expert system can be simply used in software tools for the design and simulation of the hyperchaotic Chen system and other hyperchaotic systems.