Automated trading based on biclustering mining and fuzzy modeling

Wood is a natural derivative, it possesses randomly distributed inherent defects all over its mass. This complicates the cost estimation process; data collected showed that even planks with similar defect pattern would have different percentage of material loss. Such uncertain loss was caused by changes in cutting parameters. In this study, a Fuzzy Inference (FI) method was employed to predict wood loss in a rubber wooden toy manufacturing cutting process. Notable variables are: length of cut, and area of cut. Prediction accuracy from the FI method was compared with that of other alternative methods. Common practice assumes a constant defective proportion, resulting in inaccurate cost estimation. A regression equation allows the loss to be varied by the cutting parameters; but only one parameter was found to be significant. Experimental results show that the FI method greatly outperformed regression and conventional methods. These findings emphasize the influence of cutting parameters on product cost. Accurate cost estimation enables better planning for efficient pricing strategies and enhances business competitiveness.

This paper presents an experimental of fuzzy logic with Mamdani's fuzzy inference method. This experimental applied to microcontroller using the C programming. So far, fuzzy logic with Mamdani's fuzzy inference method many applied only at the level of simulation. The experiment was intended to compare the simulation matlab with microcontroller programming. In programming the microcontroller used a methodology that can produce the same simulation results. With this experiment is expected to help the programmer microcontroller to implement fuzzy logic with Mamdani method. And this method is expected to be applied to the control system in the field of electronics that require fuzzy logic system.

To improve real-time control performance and reduce possible negative impacts of photovoltaic (PV) systems, an accurate forecasting of PV output is required, which is an important function in the operation of an energy management system (EMS) for distributed energy resources. In this paper, a weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented. The proposed approach comprises classification, training, and forecasting stages. In the classification stage, the self-organizing map (SOM) and learning vector quantization (LVQ) networks are used to classify the collected historical data of PV power output. The training stage employs the support vector regression (SVR) to train the input/output data sets for temperature, probability of precipitation, and solar irradiance of defined similar hours. In the forecasting stage, the fuzzy inference method is used to select an adequate trained model for accurate forecast, according to the weather information collected from Taiwan Central Weather Bureau (TCWB). The proposed approach is applied to a practical PV power generation system. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional ANN methods.

Fuzzy inference has a multigranular architecture consisting of symbols and continuous values, and has worked well to incorporate experts' know-how into fuzzy controls. Stability analysis of fuzzy control systems is one of the main topics of fuzzy control. A recently proposed stability analysis method on the symbolic level opened the door to the design of stable fuzzy controller using symbols. However the validity of the stability analysis in the symbolic system is not guaranteed in the continuous system. To guarantee this validity, a nonseparate condition has been introduced. If the fuzzy control system is asymptotically stable in the symbolic system and the system satisfies the nonseparate condition, the continuous system is also asymptotically stable. However this condition is too conservative. The new condition called a relaxed nonseparate condition has been proposed and the class of control systems with guaranteed discretization has been expanded. However the relaxed condition has been applicable only to controf systems having symmetric membership functions. This paper presents a new fuzzy inference method that makes the relaxed condition applicable to fuzzy control systems with asymmetric membership functions. Simulations are done to demonstrate the effectiveness of the new fuzzy inference method. The proof of the expansion of the relaxed nonseparate condition is also given.

The blood pressure signal is weak nonstationary biomedicine signal. Wavelet analysis is used to pick up the character of blood pressure signal for useful information and the truth of the oscillation amplitude of artery oscillation curve is reckoned by fuzzy inference method. Sequentially, the exact relation between the cuff pressure and the oscillation amplitude is developed and this relation is fitted with recursive least square method. Finally the systolic pressure and the diastolic pressure are calculated with the wave amplitude threshold method. With wavelet analysis and fuzzy inference method, the interference resulted from cardiovascular disease or movement on measuring is effectively decreased and the accuracy of blood pressure measurement is improved

Rate of penetration (ROP) prediction is crucial for the drilling optimization and cost-savings. In this paper, a novel drilling ROP prediction method is proposed and the prediction model can be divided into two stages (data pre-processing and T-S fuzzy inference modeling). In the first stage, four data pre-processing techniques (Reduction, re-sampling, wavelet filtering, and normalization) are used step by step to improve the quality of drilling data. In the second stage, T-S fuzzy inference method is introduced to establish the ROP prediction model. The experiment is executed by using the data from actual drilling process and the results demonstrate the effectiveness of proposed method in prediction accuracy compared with two conventional methods (response surface method and support vector regression).

The attributes in database are very important information. However, sometimes they cannot be applied efficiently. What we concern about is how to make use of them as much as possible. AFS theory is a new analytic method of fuzzy mathematics. The membership functions of AFS theory are obtained by a uniform arithmetic according to the original data. AFS theory and closeness degree functions are combined to analyze the relation of the attributes in database. Using AFS theory and fuzzy inference methods, a new fuzzy inference method is proposed. At the same time, we get a method of attribute reduction. The results are very similar to human intuition and show that the methods are convenient for data mining and attributes analyzing.

Ozone forecast models using nonlinear regression (NLR) have been successfully applied to daily ozone forecast for seven metro areas in Kentucky, including Ashland, Bowling Green, Covington, Lexington, Louisville, Owensboro, and Paducah. In this study, the updated 2005 NLR ozone forecast models for these metro areas were evaluated on both the calibration data sets and independent data sets. These NLR ozone forecast models explained at least 72% of the variance of the daily peak ozone. Using the models to predict the ozone concentrations during the 2005 ozone season, the metro area mean absolute errors (MAEs) of the model hindcasts ranged from 5.90 ppb to 7.20 ppb. For the model raw forecasts, the metro area MAEs ranged from 7.90 ppb to 9.80 ppb. Based on previously developed NLR ozone forecast models for those areas, Takagi-Sugeno fuzzy system models were developed for the seven metro areas. The fuzzy "c-means" clustering technique coupled with an optimal output predefuzzification approach (least square method) was used to train the Takagi-Sugeno fuzzy system. Two types of fuzzy models, basic fuzzy and NLR-fuzzy system models, were developed. The basic fuzzy and NLR-fuzzy models exhibited essentially equivalent performance to the existing NLR models on 2004 ozone season hindcasts and forecasts. Both types of fuzzy models had, on average, slightly lower metro area averaged MAEs than the NLR models. Among the seven Kentucky metro areas Ashland, Covington, and Louisville are currently designated nonattainment areas for both ground level O 3 and PM 2.5 . In this study, summer PM 2.5 forecast models were developed for providing daily average PM 2.5 forecasts for the seven metro areas. The performance of the PM 2.5 forecast models was generally not as good as that of the ozone forecast models. For the summer 2004 model hindcasts, the metro-area average MAE was 5.33ìg/m 3 . Exploratory research was conducted to find the relationship between the winter PM 2.5 concentrations and the meteorological parameters and other derived prediction parameters. Winter PM 2.5 forecast models were developed for seven selected metro areas in Kentucky. For the model fits, the MAE for the seven forecast models ranged from 3.23 ìg/m 3 to 4.61 ìg/m 3 (~26-28% NMAE). The fuzzy technique was also applied on PM 2.5 forecast models to seek more accurate PM 2.5 prediction. The NLR-fuzzy PM 2.5 had slightly better performance than the NLR models.

This paper presents fuzzy control method (Mamdani’s fuzzy inference method) for maximum power point tracking (MPPT) of photovoltaic (PV) system under varying irradiation and temperature conditions. The fuzzy control method has been compared with perturb and observe (P&O) method as one of the most widely conventional method used in this area. The different techniques have been analyzed and simulated. Fuzzy technique gives better and more reliable control for this application.KeywordsPhotovoltaicPerturb and observFuzzy controlMPPT

The objective of this research is to know the best financial performance and the financial performance ratio of multifinance companies listed at Indonesian Stock Exchange reviewed from the analysis of liquidity ratio, solvency ratio, activity ratio and profitability ratio. The method applied is descriptive and comparative methods. The result based on comparative method the ratio in 5 years calculated by the liquidity ratio of the company having the best CR is CFIN because it has the highest ratio of 230.15% and the best CSR is WOMF by 13.44%. The best solvency ratio is CFIN with the lowest DAR and DER ratio is 55.9% and 126.93%. Activity ratio result indicates the best RTO value is WOMF because it has the highest ratio by 0.37 times and the best TATO is ADMF with highest ratio of 0.32 times. The best profitability ratio is achieved by ADMF because it has the highest ROA and ROE ratios of 4.62% and 23.21%. Of total score in 2015-2019 periods, the first rank is occupied by ADMF with score 64, CFIN in the second one with score 84 and WOMF in the third with score 92.

In this paper, a fuzzy inference method is proposed for short-term load forecasting. A new technique of parallel tabu search is used to deal with one-day ahead prediction of daily maximum loads. This paper focuses on a fuzzy inference approach due to good understanding of the nonlinear behavior of the model. Fuzzy rules help power system operators to explain their experiences and rules in an intuitive sense. In this paper, parallel tabu search is used to globally optimize the number and location of the fuzzy membership functions. It considers two strategies of the neighborhood decomposition and multiple tabu lengths so that computational efficiency and solution accuracy are improved. The proposed method makes use of the simplified fuzzy inference to alleviate computational effort for calculating the fuzzy membership functions of the output variables. The effectiveness of the proposed method is demonstrated with real data of Chubu Electric Power Company.

A DNA sequence alignment algorithm using quality information and a fuzzy inference method

A new on-line tool wear states detecting method, with spindle and feed current signal in boring, is presented. By analyzing the effects of tool wear, as well as the cutting parameters on the current signals, the models of the relationship between the current signals and the cutting parameters are established under different tool wear states with partial experimental design and regression analysis. Fuzzy classification method is then used to obtain the membership degree of each tool wear classification with measured spindle and feed current values. Finally, the membership results of the spindle current and feed current are fused by the fuzzy inference method, and the tool wear state may be detected effectively. The validity and reliability of the method are verified by experimental results. The method can be effectively employed in practice.

Flood is commonly known as one of the most frequent type of natural disaster worldwide that occurred when the ground is not able to absorb and accommodate the heavy rainfall. Flood might be caused by increased levels of river water more than the river bank or the dams. Many methods have been proposed to forecast the rainfall distribution but mostly the forecasting accuracy of the existing method are questionable. In this paper, a fuzzy time series method was proposed to forecast the rainfall distribution. The objectives of this paper, first is to formulate fuzzy spatial forecasting model for rainfall distribution for each month in Perlis. Second, to predict the accurate rainfall values in future for early warning of flood in order to reduce flood issues. Using the fuzzy spatial forecasting method, the historical data of rainfall in Perlis were used to forecast. After that, several rules was applied to determine whether the rainfall forecasting trend value goes downward or upward movement Then, the mean square error (MSE) was calculated to compare the forecasting rainfall results of various forecasting method. The smaller the value of MSE, the better the forecasting model. The monthly historical rainfall distribution in Perlis for 4 years had been used to illustrate the forecasting algorithm of the new fuzzy time series method. The experimental results of this research exhibited higher forecasting accuracy for forecasting rainfall compare to existing methods.

in this paper, the fuzzy control and the PID control are combined, and the fuzzy inference method is utilized to realize the on-line auto-tuning of PID parameters. By applying the fuzzy control toolbox and the Simulink in Matlab , it takes the electric steering gear as the object to simulate. By setting the fuzzy auto-tuning controller, the paper draws a conclusion that this method surpasses the conventional PID with higher control accuracy, smaller overshoot, and better dynamic performance.