A genetic algorithm is used for the topology design of feed-forward neural networks. The generated neural network topology populations are then trained to approximate non-linear relationships of multiple variables. A specifically designed fitness function using the epistemological principle of dimensional homogeneity is used for the evaluation of the individual neural network generations and fo...

In this paper we propose a method for solving some well-known classes of Lane-Emden type equations which are nonlinear ordinary differential equations on the semi-innite domain. The proposed approach is based on an Unsupervised Combined Articial Neural Networks (UCANN) method. Firstly, The trial solutions of the differential equations are written in the form of feed-forward neural networks cont...

Prediction of protein secondary structure from the amino acid sequence is a classical bioinformatics problem. Common methods use feed forward neural networks or SVM’s combined with a sliding window, as these models does not naturally handle sequential data. Recurrent neural networks are an generalization of the feed forward neural network that naturally handle sequential data. We use a bidirect...

The convex hull of any subset o f vertices of an n-dimensional hypercube contains no other vertex of the hypercube. This result permits the application of some theorems of n-dimensional geometry lo digital reed-forward neural networks. Also. the construction Of the convex hull is proposed as an alternative to more traditional learning algorithms. Some preliminary simulation results are reponed.

The higher share of renewable energy sources in the electrical grid and electrification significant sectors, such as transport heating, are imposing a tremendous challenge on operation system due to increase complexity, variability uncertainties associated with these changes. recent advances computational technologies ever-growing data availability allowed development sophisticated efficient al...

In this paper we are finding input-output dependencies of feed-forward neural network which usually behaves as black box. It is very important and difficult to find or evaluate those especially for multi-input/output data approximation. We will use small be trained on a given in MATLAB Mathworks. Network simulated standalone .NET application.

Due to the rapid expansion and advancements of computer network, security has become a vital issue for modern computer network. The network intrusion detection systems play the vital role in protecting the computer networks. So, it has become a significant research issue. In spite of notable progress in intrusion detection system, there are still many opportunities to improve the existing syste...

This paper explores the performance of Google's Edge TPU on feed forward neural networks. We consider as a hardware platform and explore different architectures deep network classifiers, which traditionally has been challenge to run resource constrained edge devices. Based use joint-time-frequency data representation, also known spectrogram, we trade-off between classification energy consumed f...

This paper presents our experiences with the use of feed forward neural networks for piano chord recognition and polyphonic piano music transcription. Our final goal is to build a transcription system that would transcribe polyphonic piano music over the entire piano range. The central part of our system uses neural networks acting as pattern recognisers and extracting notes from the source aud...