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The primary objective of steelmaking through Basic Oxygen Furnace (BOF) process is to achieve desired end point carbon content, temperature and percentage composition at the lowest cost and in the shortest possible time. As of now, most widely used models for prediction of parameters of converter steelmaking are mechanistic model, statistical model and neural network model for the prediction of the...
This paper presents an adaptive-network-based fuzzy inference system (ANFIS) for long-term natural Electric consumption prediction. Six models are proposed to forecast annual Electric demand. 104 ANFIS have been constructed and tested in order to finding best ANFIS for Electric consumption. The proposed models consist of input variables such as Gross Domestic Product (GDP) and Population (POP). All...
Product image form design, which focuses on customers' psychological demands, is arousing attention increasingly. This paper presents a novel approach of customer-oriented design for translating customers' kansei image into product design elements. The most influential form elements are identified through rough set theory. Based on this, the relationship between the key form elements and customers'...
Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays, because of their flexibility, symbolic reasoning, and explanation capabilities. Meanwhile, accurate forecasts on tourism demand and study on the pattern of the tourism demand from various origins is essential for the tourism-related...
This paper shows inflow forecasting models in the Sobradinho hydroelectric plant which are based on artificial intelligence tools: ANN and fuzzy logic. In the first models two ANNs were chosen to forecast the monthly inflow in the period of one year ahead; in the second, an ANN and an ANFIS (Adaptive Neuro-Fuzzy Inference System) were used to accomplish the forecasting in the period of one and two...
The increased utilization of flexible structure systems, such as flexible manipulators and flexible aircraft in various applications, has been motivated by the requirements of industrial automation in recent years. Robust optimal control of flexible structures with active feedback techniques requires accurate models of the base structure, and knowledge of uncertainties of these models. Such information...
Coordinated Control System(CCS) in thermal power plant is a system with big inertia, large time delay and slow parameter variance as well as the property of fast parameter variance while the unit load changes. Conventional PID controller and Direct Energy Balance(DEB) Strategy which is tuned at typical operating point can hardly work well at different unit load. By using the resource of Automatic...
Forecasting exchange rate is very important for many international agents e.g. investors, money managers, investment banks, funds makers and others. We forecasted the daily Bangladeshi exchange rate series for the period of January 1992 to March 2009 using popular non-linear forecasting models, namely Markov switching autoregressive model, fuzzy extension of artificial neural network model (ANFIS)...
In this paper we have applied the adaptive neuro-fuzzy inference system (ANFIS) which is realized by an appropriate combination of fuzzy systems and neural networks for forecasting a set of input and output data of Internet traffic time series. Several statistical criteria are applied to provide the effectiveness of this model. The obtained results demonstrate that the ANFIS model present a good precision...
This study tries to examine the impacts of emotional learning based fuzzy inference system (ELFIS) on completion time of projects. For the project management team, on time delivery within budget is a fundamental and important factor that highlights the importance of estimating the completion time of a project during its execution. This study implies four soft computing methods which are artificial...
In this paper, we developed a model based on the adaptive neuro-fuzzy inference systems (ANFIS) for analyzing a real non Gaussian process. The obtained results show that the generated values using ANFIS techniques have similar statistical characteristics as real data. Additionally, the developed model fits well real data and can be used for predicting purpose. Compared with existing model obtained...
Accurate short-term traffic flow prediction has become a critical problem in intelligent transportation systems (ITS). In the paper, a kind of adaptive prediction method for short-term traffic flow based on ANFIS (adaptive-network-based fuzzy interference system) model was presented. ANFIS is a fuzzy interference tool implemented in the framework of adaptive network. It combines the comprehensibility...
Modeling of non-linearity and uncertainty associated with rainfall-runoff process has received a lot of attention in the past years. Recently artificial intelligence techniques are used for hydrological time series modelling. Earlier studies showed this approach is effective, still there are concerns about how these techniques perform efficiently to predict the run-off with high standard of accuracy...
Past few decades have seen a resurgent trend towards establishment of intelligent manufacturing systems, which are capable of using advanced knowledge-bases and intelligence techniques in aiding critical operational procedures in manufacturing. Increasing demands on productivity and quality with the increase in global competitiveness have necessitated development of sound predictive models and optimization...
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