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

To cope with resource constraints in multitasking control systems, feedback scheduling is emerging as an important technique for integrating control and scheduling. The ability of neural networks (NNs) as good and robust nonlinear function approximators, reducing the computation time as compared against algorithmic solutions, suggests applying them to the feedback scheduling problem. A novel, s...

The integration of different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the hybridization or fusion of these techniques has, in recent years, contributed to a large number of new intelligent system designs. Computational intelligence is an innovative framework for constructing intelligent hybrid architectures involving Neural ...

A new class of Neural Networks (NN), designated the Multiple Feed-Forward (MFF) networks, and a new gradient-based learning algorithm, Multiple Back-Propagation (MBP), are proposed and analyzed. MFF are obtained by integrating two feed-forward networks (a main network and a space network) in a novel manner. A major characteristic is their ability to partition the input space by using selective ...

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...

Continuous-time Hopfield network has been an important focus of research area since 1980s whose applications vary from image restoration to combinatorial optimization from control engineering to associative memory systems. On the other hand, in wireless communications systems literature, power control has been intensively studied as an essential mechanism for increasing the system performance. ...

This paper describes experimental investigations for exploring the dependence of Neural Networks behavior and capabilities on their complexity. Characteristic behavior patterns are worked out through an artificial problem. We analyze in particular the dependency of overfitting on NN complexity, characterize the bias-variance evolution of the error, and the effects of two regularization techniqu...

Residual generation is an essential part of model-based fault detection schemes. For nonlinear systems, the task of residual generation is sometimes complicated by the size of the problem, or by the lack of a suitable model from where the residual can be generated. This paper develops and implements neural-networks based system identification techniques for nonlinear systems with the specific g...

Conceptual cost estimates are important to project feasibility studies, even the final project success. The estimates provide significant information for project evaluations, engineering designs, cost budgeting and cost management. This study proposes an artificial intelligence approach, the evolutionary fuzzy hybrid neural network (EFHNN), to improve precision of conceptual cost estimates. The...

Wireless sensor networks(WSN) are an exiting emerging technology that scientists believe to become a part of every day life in the next few years. However, at this time many issues in wireless sensor networks remain unresolved. This paper studies the architecture of a neural wireless sensor network designed to identify technical condition of the base station of wireless sensor networks ,and thi...