375 research outputs found

    Estimating the global temperature change by means of a fuzzy logic model obtained from a simple climate model

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    Ponencia presentada en: VI Congreso Internacional de la Asociación Española de Climatología celebrado en Tarragona del 8 al 11 de octubre de 2008.[EN]In this work a simple box model of the ocean-atmosphere system is used to asses the response of the simulated global mean temperature to expected changes in the surface thermal forcing at the year 2000, as well as to variations of two key parameters, namely ocean thermal diffusivity and the atmospheric feedback. Such experiments provide the input data needed to build fuzzy logic models that are able to deal with the uncertainties associated to the model parameters. Two fuzzy logic approaches are presented in this article.[ES]En este trabajo se utiliza un modelo de cajas del sistema océano-atmósfera para estudiar la respuesta de la temperatura promedio global a cambios en el forzamiento radiativo y a la variación de dos parámetros importantes del modelo: la difusividad térmica del océano y la sensitividad de la atmósfera (procesos de retroalimentación). A partir de los campos de temperatura obtenidos, se construyen dos modelos basados en lógica difusa

    A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

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    Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients

    Flexible information retrieval: some research trends

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    In this paper some research trends in the field of Information Retrieval are presented. The focus is on the definition of flexible systems, i.e. systems that can represent and manage the vagueness and uncertainty which is characteristic of the process of information searching and retrieval. In this paper the application of soft computing techniques is considered, in particular fuzzy set theory

    On neuro-fuzzy applications for automatic control, supervision, and fault diagnosis for water treatment plant

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    Water treatment includes many complex phenomena, such as coagulation and flocculation. These reactions are hard or even impossible to control satisfyingly by conventional methods. Biological water treatment systems are difficult to model because their performance is complex and varies significantly with different reactor configurations, influent characteristics, and operational conditions. Neuro-fuzzy ANFIS method, which is chosen as the method in this case, is a new intelligent method in this line of process industry. Although intelligent tools such as neural network, fuzzy logic and neuro-fuzzy methods have been applied in real time water treatment plant for some time, problems of monitoring water treatment processes and assessing uncertainty for the coagulant dosing rate represent a major challenged that need to be investigated. In this research, statistical methods are used to analyze nonstationary time series water treatment process where they are accrued from a neuro-fuzzy ANFIS model. The proposed scheme is evaluated in computer simulation studies using real process data before application to the real plant

    APPLICATION OF SOFT COMPUTING TECHNIQUES OVER HARD COMPUTING TECHNIQUES: A SURVEY

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    Soft computing is the fusion of different constituent elements. The main aim of this fusion to solve real-world problems, which are not solve by traditional approach that is hard computing. Actually, in our daily life maximum problem having uncertainty and vagueness information. So hard computing fail to solve this problems, because it give exact solution. To overcome this situation soft computing techniques plays a vital role, because it has capability to deal with uncertainty and vagueness and produce approximate result. This paper focuses on application of soft computing techniques over hard computing techniques

    Transductive-Weighted Neuro-fuzzy Inference System for Tool Wear Prediction in a Turning Process

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    This paper presents the application to the modeling of a novel technique of artificial intelligence. Through a transductive learning process, a neuro-fuzzy inference system enables to create a different model for each input to the system at issue. The model was created from a given number of known data with similar features to data input. The sum of these individual models yields greater accuracy to the general model because it takes into account the particularities of each input. To demonstrate the benefits of this kind of modeling, this system is applied to the tool wear modeling for turning process.This work was supported by DPI2008-01978 COGNETCON and CIT-420000-2008-13 NANOCUT-INT projects of the Spanish Ministry of Science and Innovation.Peer reviewe

    Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

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    This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments

    Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

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    Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model
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