This paper presents a novel population prediction algorithm based on modular neural network (PA-MNN) for handling dynamic multi-objective optimization. The proposed consists of three mechanisms. First, we set up (MNN) and train it with historical information. Some the initial solutions are generated by MNN when an environmental change is detected. Second, some predicted forward-looking center p...

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The desig...

In several modular neural network (MNN) architectures, the individual decisions at the module level have to be integrated together using a voting scheme. All these voting schemes use the outputs of the individual modules to produce a global output without inferring explicit information from the problem feature space. This makes the choice of the aggregation procedure very subjective. In this wo...

To embed modularity (i.e. to perform a local and encapsulated computation) into neural networks (NN) leads to many advantages. Hence, the development of a general model of modular neural networks (MNN) will enable a broader use of Neural Networks (NN). However, some important issues remain to be solved to enable a systematic use of MNN. In a practical point of view, the most important matter co...

In this paper we propose a new model of a Modular Neural Network (MNN) with fuzzy integration based on granular computing. The topology and parameters of the model are optimized with a Hierarchical Genetic Algorithm (HGA). The model was applied to the case of human recognition to illustrate its applicability. The proposed method is able to divide the data automatically into sub modules, to work...

Modular Neural Networks (MNNs) is a rapidly growing field in artificial Neural Networks (NNs) research. This paper surveys the different motivations for creating MNNs: biological, psychological, hardware, and computational. Then, the general stages of MNN design are outlined and surveyed as well, viz., task decomposition techniques, learning schemes and multi-module decision-making strategies. ...

This paper presents an empirical evaluation of a number of recently developed Automatic Target Recognition algorithms for Forward-Looking Infrared (FLIR) imagery using a large database of real FLIR images. The algorithms evaluated are based on convolutional neural networks (CNN), principal component analysis (PCA), linear discriminant analysis (LDA), learning vector quantization (LVQ), modular ...

In this paper, inspired by the mechanism of memory and prediction in our brains [2], we propose a straightforward and effective memorybased gait recognition method (MGR) to realize the memory and recognition process of the gait sequences. Because of various covariates including carrying, clothing, surface and view angle, we extract the robust 2D joint location information via the joint extracti...

Physiological studies suggest that the integration of neural circuits and biomechanics (e.g., muscles) is a key for animals to achieve robust and efficient locomotion over challenging surfaces. Inspired by these studies, we present a neuromechanical controller of a hexapod robot for walking on soft elastic and loose surfaces. It consists of a modular neural network (MNN) and virtual agonist-ant...