Deep learning using multi-layer neural networks (NNs) architecture manifests superb power in modern machine learning systems. The trained Deep Neural Networks (DNNs) are typically large. The question we would like to address is whether it is possible to simplify the NN during training process to achieve a reasonable performance within an acceptable computational time. We presented a novel appro...

Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the...

For managing data in a smart card’s limited memory, containing medical and biometric images, images compression is resorted to. For image retrieval, it is necessary that the classification algorithm be efficient to search and locate the image in a compressed domain. This study proposes a novel training algorithm for Multi-Layer Perceptron Neural Network (MLP-NN) to classify compressed images. M...

Quality improvement provides organizations with significant opportunities to reduce costs, increase sales, provide on time deliveries and foster better customer relationships. The design and manufacturing are among the critical processes for continuous quality improvement. Time series data collected from these processes are the useful source. While there are various techniques to explore these ...

In this work neural and neuro-fuzzy controllers are developed for the inverters of Uninterruptible Power Supplies (UPS) to improve their transient response and adaptability to various loads. Idealized load-currentfeedback controller is built to obtain example patterns for training the networks. Example patterns under various loading conditions are used in the off-line training of the selected n...

Artificial Neural Networks or widely known as Neural networks (ANNs or NNs) is a computational paradigm that comprises of mathematical, statistical, biological sciences and philosophy. These paradigms formulate a formula to form a brain like function, called artificial neuron. Artificial neuron comprises of large number of computational processing elements called units, nodes or cells. Analogou...

After production and operations, finance and investments are one of the most frequent areas of neural network applications in business. The lack of standardized paradigms that can determine the efficiency of certain NN architectures in a particular problem domain is still present. The selection of NN architecture needs to take into consideration the type of the problem, the nature of the data i...

Over the last few years, connectionism or neural networks (NN) have successfully been applied to a wide range of areas and have demonstrated their capabilities in solving complex problems. Current indications show that these techniques are very important and rapidly developing areas of research and applications, particularly, in the area of data mining for knowledge discovery. One particular ne...

Combining multiple classiiers is an eeective technique for improving accuracy. There are many general combining algorithms, such as Bagging or Error Correcting Output Coding, that signiicantly improve classiiers like decision trees, rule learners, or neural networks. Unfortunately, many combining methods do not improve the nearest neighbor classiier. In this paper, we present MFS, a combining a...