We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms for pattern recognition. Modular Neural Networks (MNN) have shown significant learning improvement over single Neural Networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more diffi...

In this paper, modular neural network (MNN) inversion has been applied for the parameters approximation of the gravity anomaly causative target. The trained neural network is used for estimating the amplitude coefficient and depths to the top and bottom of a finite vertical cylinder source. The results of the applied neural network method are compared with the results of the least-squares stand...

We present a Modular Neural Network (MNN) for phoneme recognition within the framework of a hybrid system (neural networks and HMMs) for speakerindependent single word recognition. With this approach, we are taking the computational effort into account which is used as an additional criterion for assessing the system performance. The main idea of the proposed MNN is the distribution of the comp...

The use of \global neural networks" (as the back propagation neural network) and \clustering neural networks" (as the radial basis function neural network) leads each other to diierent advantages and inconvenients. The combination of the desirable features ot those two neural ways of computation is achieved by the use of Modular Neural Networks (MNN). In addition, a considerable advantage can e...

Classification is a challenging task that has important application in real life and its application are excepted to grow more in future. In this paper, we analyze the effectiveness of Modular Neural Network as a modelling tool for data classification. The MNN classifier outperforms the surveyed nets due to its novel task decomposition and multi-module decision-making techniques. In this paper,...

The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number cyber-attacks is also increasing. A detection systems are used in an attempt to detect known attacks using signatures network traffic. In recent years, researchers different machine learning methods without relying on those signatures. generally high false-positi...

To better support forest sustainable management, this paper explores the technical framework for forest health assessment, and then focuses on how to better execute the evaluation part of this technical framework. Modular neural networks (MNN) have been shown to be more efficient for classification problems than the conventional monolithic artificial neural network. Therefore, this was used in ...

Artificial neural networks have proven to be an attractive mathematical tool to represent complex relationships in many branches of hydrology. Due to this attractive feature, neural networks are increasingly being applied in subsurface modeling where intricate physical processes and lack of detailed field data prevail. In this paper, a methodology using modular neural networks (MNN) is proposed...

In spite of the advancement in computerized imaging, many image modalities produce images with commotion influencing both visual quality and upsetting quantitative analysis. this way, research zone denoising is very dynamic. Among an extraordinary assortment restoration techniques neural network system-based noise suppression a basic productive methodology. paper, Bilateral Filter (BF) based Mo...

In this paper, we present a new kind of learning implementation to recognize the patterns using the concept of Mirroring Neural Network (MNN) which can extract information from distinct sensory input patterns and perform pattern recognition tasks. It is also capable of being used as an advanced associative memory wherein image data is associated with voice inputs in an unsupervised manner. Sinc...