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...

This paper presents an application of an artificial neural network to determine survival time of patients with a bladder cancer. Different learning methods have been investigated to find a solution, which is most optimal from a computational complexity point of view. In our study, a model of a multilayer perceptron with a training algorithm based on an error back-propagation method with a momen...

We propose anti-spam filtering methods for agglutinative languages in general and for Turkish in particular. The methods are dynamic and are based on Artificial Neural Networks (ANN) and Bayesian Networks. The developed algorithms are user-specific and adapt themselves with the characteristics of the incoming e-mails. The algorithms have two main components. The first one deals with the morphol...

An integrated Neural Network and Gravitational Search Algorithm (HNNGSA) are used to solve Blasius differential equation. To aim this purpose, GSA technique is applied to train a multi-layer perceptron neural network, which is used as approximation solution of the Blasius differential equation. A trial solution of the differential equation is written as sum of two parts. The first part satisfie...

This paper presents an FPGA implementation of a 3-layer perceptron using the FDFM (Few DSP blocks and Few block RAMs) approach implemented in the Xilinx Virtex-6 family FPGA. In the FDFM approach, multiple processor cores with few DSP slices and few block RAMs are used. We have implemented 150 processor cores for perceptrons in a Xilinx Virtex-6 family FPGA XC6VLX240T-FF1156. The implementation...

A method for evolving behavior-based robot controllers using genetic programming is presented. Due to their hierarchical nature, genetic programs are useful representing high-level knowledge for robot controllers. One drawback is the difficulty of incorporating sensory inputs. To overcome the gap between symbolic representation and direct sensor values, the elements of the function set in genet...

-It is widely believed that the back propagation algorithm in neural networks, for tasks such as pattern classification, overcomes the limitations of the perceptron. We construct several counterexamples to this belief. We also construct linearly separable examples which have a unique minimum which fails to separate two families of vectors, and a simple example with four two-dimensional vectors ...