Abstract— An educational platform is presented here for the beginner students in the Simulation and Artificial Intelligence sciences. It provides with a start point of building and simulation of the manipulators, especially of 2R planar Robot. It also displays a method to replace the inverse kinematic model (IKM) of the Robot with a simpler one, by using a Multi-Layer Perceptron Neural Network ...

The main aim of this study, is to evaluate the seismic reliability of steel concentrically braced frame (SCBF) structures optimally designed in the context of performance-based design. The Monte Carlo simulation (MCS) method and neural network (NN) techniques were utilized to conduct the reliability analysis of the optimally designed SCBFs. Multi-layer perceptron (MLP) trained by back propagati...

In this paper, we introduce a Takagi-Sugeno (TS) fuzzy model which is derived from a typical Multi-Layer Perceptron Neural Network (MLP NN). At first, it is shown that the considered MLP NN can be interpreted as a variety of TS fuzzy model. It is discussed that the utilized Membership Function (MF) in such TS fuzzy model, despite its flexible structure, has some major restrictions. After modify...

Brain-Computer Interfaces (BCIs) measure brain signals activity, intentionally and unintentionally induced by users, and provides a communication channel without depending on the brain’s normal peripheral nerves and muscles output pathway. Feature Selection (FS) is a global optimization machine learning problem that reduces features, removes irrelevant and noisy data resulting in acceptable rec...

The inherent black-box nature of neural networks is an important drawback with respect to the problem of knowledge discovery from databases. In this work our aim is to extract rules from multi-layer perceptrons. This is a starting point to explain the basis of neural network solutions for knowledge discovery to experts of domain applications. Our approach consists in characterizing discriminant...

introduction: evoked potentials arisen by stimulating the brain can be utilized as a communication tool  between humans and machines. most brain-computer interface (bci) systems use the p300 component,  which is an evoked potential. in this paper, we evaluate the use of the hidden markov model (hmm) for  detection of p300.  materials and methods: the wavelet transforms, wavelet-enhanced indepen...

there is no accurate data for the bank cards fraud in iran. but, it seems to be a growing trend in this regard and in the near future it is going to become one of the critical problems in iran's banking system. unfortunately, not enough research works have been done in this field in our country and the banking system requires models that are efficient enough to ensure safe use of bank card...

Mini-batch based Stochastic Gradient Descent(SGD) has been widely used to train deep neural networks efficiently. In this paper, we design a general framework to automatically and adaptively select training data for SGD. The framework is based on neural networks and we call it Neural Data Filter (NDF). In Neural Data Filter, the whole training process of the original neural network is monitored...

solar radiation data play an important role in solar energy relevant researches. these data are not available for some locations due to the absence of the meteorological stations. therefore, solar radiation data have to be predicted by using solar radiation estimation models. this study presents an integrated artificial neural network (ann) approach for estimating solar radiation potential over...

Speaker authentication has been developed rapidly in the last few decades. This research work attempts to extract the hidden features of human voice that is able to simulate human auditory system characteristics in speaker authentication. The hidden features are then presented as inputs to a Multi-Layer Perceptron Neural Network and Generic Self-organizing Fuzzy Neural Network to verify the spe...