By increasing use of computer network and internet using Intrusion Detection System has become more popular. The main drawback of IDS is to generate alert to system administrator based on malicious activities that violates security policies. Recently fuzzy logic plays a vital role in detecting attacks using various rule generation technique. This paper proposed a new concept of using various fu...

Soft Computing models play an important role in the field of recognition, classification, data prediction, etc in various application fields. Soft Computing models include fuzzy logic, neural, network, genetic algorithm, particle swarm optimization, Bacterial forging algotithm, classification and clustering, etc., the extraction of hidden information from large database is possible through the ...

permeability can be directly measured using cores taken from the reservoir in the laboratory. due to high cost associated with coring, cores are available in a limited number of wells in a field. many empirical models, statistical methods, and intelligent techniques were suggested to predict permeability in un-cored wells from easy-to-obtain and frequent data such as wireline logs. the main obj...

Artificial neural network models have been studied for many years with the hope of designing information proeessing systems to produee human-like performance. The present artiele provides an introduction to neural computing by reviewing three commonly used models (namely, Hopfield's model, Kobonen's model and the Multi-layer perceptron) and showing their applications in various fields such as p...

This paper implements a Neuro-Fuzzy (FNN) approach to autonomously navigate a car-like robot in an unknown environment. The applied technique allows the robot to avoid obstacles and locally search for a path leading to the goal after learning and adaptation. It is based on two Fuzzy Artmap neural networks, a Reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC)...

In this paper a new backpropagation learning method enhanced with type-2 fuzzy logic is presented. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. In this work, type-2 fuzzy inference systems are used to obtain the ...

Permeability prediction problem has been examined using several methods such as empirical formulas, regression analysis and intelligent systems especially neural networks and fuzzy logic. This study proposes an improved and novel model for predicting permeability from conventional well log data. The methodology is integration of empirical formulas, multiple regression and neuro-fuzzy in a commi...

This paper pro oses a neural fuzzy connection admission control NFCAC) scheme, which combines of the neural-net, t o solve the connection admission control (CAC) problems in ATM networks. Recently, fuzzy logic systems have been successfully applied t o deal with the traffic control related probleims and provided a robust mathematical framework for dealing with "real-world" imprecision; multi-la...

This paper proposes a model for commonsense causal reasoning, based on the basic idea of neural networks. After an analysis of the advantages and limitations of existing accounts of causality, a fuzzy logic based formalism FEL is proposed that takes into account the inexactness and the cumulative evidentiality of commonsense causal reasoning, overcoming the limitations of existing accounts. Ana...

⎯ In this paper, a novel approach for robust trajectory tracking of induction motor drive is presented. By combining variable structure systems theory with fuzzy logic concept and neural network techniques, a new algorithm is developed. Fuzzy logic was used for the adaptation of the learning algorithm to improve the robustness of learning and operating of the neural network. The developed contr...