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Distribution fitting is a widely recurring problem in different fields such as telecommunication, finance and economics, sociology, physics, etc. Standard methods often require solving difficult equations systems or investments in specialized software. The paper presents a new approach to distribution fitting that exploits Genetic Algorithms in order to simultaneously identify the distribution type...
This article presents a clustering-based approach to fuzzy system identification. In order to construct an effective initial fuzzy model, this article tries to present a modular method to identify fuzzy systems based on a hybrid clustering-based technique. Moreover, the determination of the proper number of clusters and the appropriate location of clusters are one of primary considerations on constructing...
This paper presents a comparative assessment of Pole Placement and Linear Quadratic Regulator to control a balancing of two wheels mobile robot. Disturbance is applied to to test the balancing of the robot. The mathematical model of this robot which is highly nonlinear is derived. The final model is then represented in state space form using MATLAB/Simulink application. Simulation on MATLAB application...
This paper is devoted to efficient algorithms for real-time rendering of seashore using programmable Graphics Processing Unit (GPU). The scene of seashore is a usual component of virtual environment in simulators or games and should be realistic and real-time. We realized the realtime seashore simulation in three steps: first the ocean wave generation, using a simple but high-efficiency model which...
Electrical Capacitance Tomography (ECT) is one of the most attractive technique for industrial process imaging because of its low construction cost, safety, non-invasiveness, non-intrusiveness, fast data acquisition, simple structure, wide application field and suitability for most kinds of flask and vessels. However, image reconstruction based ECT suffers many limitations. They include the Soft-field...
The classification of imbalanced data is a well-studied topic in data mining. However, there is still a lack of understanding of the factors that make the problem difficult. In this work, we study the two main reasons that make the classification of imbalanced datasets complex: overlapping and data fracture. We present a Genetic Programming-based feature extraction method driven by Rough Set Theory...
Research on the Egyptian food security, has become the subject of countless studies and debates. The gap between the Egyptian domestic food production and consumption is translated into high import costs. In this paper, modeling, simulation analytical capability, expert experiences and imagination, and policy/decision makers' insights are integrated in a decision support system (DSS). The developed...
The concept of e-learning has become popular in recent years as higher education institutions have been introducing it to support the conventional teaching approach. However, the implementation of e-learning in technical disciplines (e.g. Robotics and Simulation) is still far behind the rapid growth of practice in narrative disciplines (e.g. Database and Theory of Computer Science). The perception...
The advantages of soft c-means over its hard and fuzzy versions render it more attractive to use in a wide variety of applications. Its main merit lies in its relatively higher convergence speed, which is more obvious in the presence of huge high dimensional data. This work presents a new approach to accelerate the convergence of the original soft c-means. It is mainly based on an iterative optimization...
Combining pattern recognition is the promising direction in designing an effective classifier systems. There are several approaches of collective decision-making, among them voting methods, where the decision is a combination of individual classifiers' outputs are quite popular. This article focuses on the problem of fuser design which uses continuous outputs of individual classifiers to make a decision...
Control of interior permanent magnet (IPMSM) is difficult because its nonlinearity and parameter uncertainty. In this paper, a fuzzy c-regression models clustering algorithm which is based on T-S fuzzy is used to model IPMSM with a series linear model and weight them by memberships. Lagrangian of constrained function is built for calculating clustering centers where training output data are considered...
Linearization of T-S fuzzy model is difficult to be achieved by using existing linearization methods because fuzzy rules and membership functions are included in T-S fuzzy models. In this paper, a new linearization method is proposed for discrete time T-S fuzzy system based on the properties of T-S fuzzy theorem. The local linear models of a T-S fuzzy model are transformed to a controllable canonical...
Research in learning and planning in real-time strategy (RTS) games is very interesting in several industries such as military industry, robotics, and most importantly game industry. A recent published work on online case-based planning in RTS Games does not include the capability of online learning from experience, so the knowledge certainty remains constant, which leads to inefficient decisions...
This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q(λ)-learning with a fuzzy inference system as a function approximation is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to two different differential games. The proposed technique is...
There is still an urgent need of finding a mathematical model which can provide an accurate relationship between the software project effort/cost and the cost drivers. A powerful algorithm which can optimize such a relationship via developing a mathematical relationship between model variables is urgently needed. In this paper, we explore the use of GP to develop a software cost estimation model utilizing...
In this paper, a T-S fuzzy modeling and tracking control method is proposed for wheeled mobile robots by using T-S fuzzy linearization approach. The proposed method has advantages in that the linear control theories can be used for the tracking control of wheeled mobile robots after linearization of them. The local linear models are converted into controllable canonical forms respectively and then...
The active magnetic bearing (AMB) presents a solution for all the technical problems of the classical bearing since it ensures the total levitation of a body in space eliminating any mechanical contact between the rotor and the stator. The goal of our work is to show the control efficiency of a magnetic sustention, characterized by its nonlinear model, using neural networks (NN). In this paper a study...
According to some biological observations, generating output variability is one of the characteristics expected from a memory model. In this paper a BAM inspired chaotic model is used to mimic this functionality of the brain. Chaos gives the potential to create deterministic variability and control its degree of uncertainty. Using some time series generated by the trained network, largest lyapunov...
Worker differences are fundamental consideration in personnel assignment which is one of the key decisions that influence the productivity and quality of assembly production. The personnel station fitness is proposed to describe the suitability of the worker for the assembly activity they assume. A personnel assignment model for assembly production is constructed, and the objectives are maximizing...
Fuzzy C-means (FCM) and Rough K-means (RKM) algorithms are two popular soft clustering algorithms that allow for overlapping clusters. The overlapping clusters can be useful in applications where restrictions imposed by crisp clustering that force assignment of every object to a unique cluster may not be practical. Likewise RKM and FCM, interval set representation of clusters would also generate overlapping...
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