In this a?ticle, we present a two stage neural network structure which combines the selforganizing map (SOM) and the multilayer perceptron (MLP) for the problem of texture classification. The ttxhrre features are extracted using a multichannel approach. These channels comprise of a set of Gaborfilters having different sizes, orientations and frequencies to constitute N-dimensional feature vecto...

The problem of the rejection of patterns not belonging to identiied training classes is investigated with respect to multilayer perceptron networks (MLPs). The reason for the inherent unreliability of the standard MLP in this respect is explained and some mechanisms for the enhancement of its rejection performance are considered. Two network conngurations are presented as candidates for a more ...

Users and organizations find it continuously challenging to deal with distributed denial of service (DDoS) attacks. . The security engineer works to keep a service available at all times by dealing with intruder attacks. The intrusiondetection system (IDS) is one of the solutions to detecting and classifying any anomalous behavior. The IDS system should always be updated with the latest intrude...

1 2 m 1 2 k 1 2 n Hash bin address in binary hash Key Compress to a shorter binary code Figure 1: Architecture of Neuro-Hasher consists of a two layer feedforward neural net. The generic function of a feedforward multilayer perceptron (MLP) network is to map patterns from one space to another. This mapping function, determined by the set of examples used to train the network, may be viewed as a...

1371 Stable On-Line Evolutionary Learning of NN-MLP Qiangfu Zhao Abstract| To design the nearest neighbor based multilayer perceptron (NN-MLP) e ciently, the author has proposed a non-genetic based evolutionary algorithm called the R4|rule. For o -line learning, the R4|rule can produce the smallest or nearly smallest networks with high generalization ability by iteratively performing four basic...

you may have heard that the Brain is plastic. As you know the brain is not made of plastic, Brain Plasticity also called Neuroplasticity. Brain plasticity is a physical process. Gray matter can actually shrink or thicken neural connections can be forged and refined or weakened and severed. Brain Plasticity refers to the brain’s ability to change throughout life. The brain has the amazing abilit...

In this paper we propose a synergistic melting of neural networks and decision trees (DT) we call neural decision trees (NDT). NDT is an architecture a la decision tree where each splitting node is an independent multilayer perceptron allowing oblique decision functions or arbritrary nonlinear decision function if more than one layer is used. This way, each MLP can be seen as a node of the tree...

In this paper, a combustion temperature retrieval approximation for high-resolution infrared ground-based measurements has been developed based on a multilayer perceptron (MLP) technique. The introduction of a selection subset of features is mandatory due to the problems related to the high dimensionality data and the worse performance of MLPs with this high input dimensionality. Principal comp...

This paper compares a newly proposed hybrid connectionist-SCHMM approach [5] with other hybrid a p proaches. In the new approach a multilayer perceptron (MLP) replaces the conventional codebooks of semicontinuous HMMs. The MLP is therefore trained on s w d k d basic elements (phones and phone parts) in such a way that the outputs of the network estimate the a posteriori probabilities of these e...

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...