Neural network computational modules have recently gained as an unconventional and useful tool for RF and microwave modeling and design. Neural network is trained to learn the behavior of Annular Ring Microstrip Antenna’s equivalent circuit parameters. A trained neural network is used for designing fast and less error answers to the task that has to be learned. In this paper, structure of Annul...

Leukaemia is a cancer of blood that causes more death than any other cancers among children and young adults under the age of 20. This disease can be cured if it is detected and treated at the early stage. Based on this argument, the requirement for fast analysis of blood cells for leukaemia is of paramount importance in the healthcare industry. This paper presents the classification of White B...

Artificial neural networks have been recognized as a powerful tool for pattern classification problems, but a number of researchers have also suggested that straightforward neural-network approaches to pattern recognition are largely inadequate for difficult problems such as handwritten numeral recognition. In this paper, we present three sophisticated neural-network classifiers to solve comple...

Neural networks (NNs) have been increasingly used in recent years for the solving complex nonlinear problems. NNs are seen as an attractive alternative to process based modeling approaches, as they are able to extract an underlying relationship from the data when knowledge of physical process is lacking. The paper evaluates the predictive power of a model, which emulates an army commander on th...

In this paprr, we dvscribe d recurrent neural network based, isolated word speech recognizer. 'The recognizer uses 2 MLP's. A f i s t , static MLP is used for classification of frames in phonemes. Next, a time compression step is applied. The resulting pseudo-segments are then used as inputs for a second, dynamic MLP that integrates the information over time to decide the current word. We apply...

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...

no unique method has been so far specified for determining the number of neurons in hidden layers of multi-layer perceptron (mlp) neural networks used for prediction. the present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. the data used in the present research for prediction are consumption data of water...

In this study, a fault diagnostic system in a multi-level inverter using a MLP network is developed. Using a mathematical model, it is difficult to diagnose a Multilevel-Inverter Drive (MLID) system, because MLID system complexity has a non-linear factor and it consist of many switching devices. Therefore neural network classification is applied to fault diagnosis of MLID system. Multilayer per...

We present the hardware implementation of partially connected neural network that is defined as an extended of the MultiLayer Perceptron (MLP) model. We demonstrate that partially connected neural networks lead to a higher performance in terms of computing speed (requiring less memory and computing resources). This work addresses a complete study that compares the hardware implementation of MLP...

In this paper, we present a comparative study of different neural network models for forecasting the weather of Vancouver, British Columbia, Canada. For developing the models, we used one year’s data comprising of daily maximum and minimum temperature and wind-speed. We used a Multi-Layered Perceptron (MLP) and an Elman Recurrent Neural Network (ERNN) trained using the one-step-secant and Leven...