10,390 research outputs found
Intelligent control based on fuzzy logic and neural net theory
In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment
APPRAISAL OF TAKAGIāSUGENO TYPE NEURO-FUZZY NETWORK SYSTEM WITH A MODIFIED DIFFERENTIAL EVOLUTION METHOD TO PREDICT NONLINEAR WHEEL DYNAMICS CAUSED BY ROAD IRREGULARITIES
Wheel dynamics play a substantial role in traversing and controlling the vehicle, braking, ride comfort, steering, and maneuvering. The transient wheel dynamics are difficult to be ascertained in tireāobstacle contact condition. To this end, a single-wheel testing rig was utilized in a soil bin facility for provision of a controlled experimental medium. Differently manufactured obstacles (triangular and Gaussian shaped geometries) were employed at different obstacle heights, wheel loads, tire slippages and forward speeds to measure the forces induced at vertical and horizontal directions at tireāobstacle contact interface. A new TakagiāSugeno type neuro-fuzzy network system with a modified Differential Evolution (DE) method was used to model wheel dynamics caused by road irregularities. DE is a robust optimization technique for complex and stochastic algorithms with ever expanding applications in real-world problems. It was revealed that the new proposed model can be served as a functional alternative to classical modeling tools for the prediction of nonlinear wheel dynamics
Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA
In this study two approaches are applied for the prediction of the economic recession or expansion periods in USA. The first approach includes Logit and Probit models and the second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell membership functions. The in-sample period 1950-2006 is examined and the forecasting performance of the two approaches is evaluated during the out-of sample period 2007-2010. The estimation results show that the ANFIS model outperforms the Logit and Probit model. This indicates that neuro-fuzzy model provides a better and more reliable signal on whether or not a financial crisis will take place.ANFIS, Discrete Choice Models, Error Back-propagation, Financial Crisis, Fuzzy Logic, US Economy
Potential of support-vector regression for forecasting stream flow
Vodotok je važan za hidroloÅ”ko prouÄavanje zato Å”to odreÄuje varijabilnost vode i magnitudu rijeke. Inženjerstvo vodnih resursa uvijek se bavi povijesnim podacima i pokuÅ”ava procijeniti prognostiÄke podatke kako bi se osiguralo bolje predviÄanje za primjenu kod bilo kojeg vodnog resursa, na pr. projektiranja vodnog potencijala brane hidroelektrana, procjene niskog protoka, i održavanja zalihe vode. U radu se predstavljaju tri raÄunalna programa za primjenu kod rjeÅ”avanja ovakvih sadržaja, tj. umjetne neuronske mreže - artificial neural networks (ANNs), prilagodljivi sustavi neuro-neizrazitog zakljuÄivanja - adaptive-neuro-fuzzy inference systems (ANFISs), i support vector machines (SVMs). Za stvaranje procjene koriÅ”tena je Rijeka Telom, smjeÅ”tena u Cameron Highlands distriktu Pahanga, Malaysia. Podaci o dnevnom prosjeÄnom protoku rijeke Telom, kao Å”to su koliÄina padavina i podaci o vodostaju, koristili su se za period od ožujka 1984. do sijeÄnja 2013. za poduÄavanje, ispitivanje i ocjenjivanje izabranih modela. SVM pristup je dao bolje rezultate nego ANFIS i ANNs kod procjenjivanja dnevne prosjeÄne fluktuacije vodotoka.Stream flow is an important input for hydrology studies because it determines the water variability and magnitude of a river. Water resources engineering always deals with historical data and tries to estimate the forecasting records in order to give a better prediction for any water resources applications, such as designing the water potential of hydroelectric dams, estimating low flow, and maintaining the water supply. This paper presents three soft-computing approaches for dealing with these issues, i.e. artificial neural networks (ANNs), adaptive-neuro-fuzzy inference systems (ANFISs), and support vector machines (SVMs). Telom River, located in the Cameron Highlands district of Pahang, Malaysia, was used in making the estimation. The Telom Riverās daily mean discharge records, such as rainfall and river-level data, were used for the period of March 1984 ā January 2013 for training, testing, and validating the selected models. The SVM approach provided better results than ANFIS and ANNs in estimating the daily mean fluctuation of the streamās flow
Analysis of investment risks as a complex system using fuzzy logic and uncertainty management methods
Purpose: In order to effectively handle complex projects, managers need to adopt a pluralistic approach to practice. They should be able to use a wide range of tools and ways of thinking to develop their own methods, their own practice models, freely, according to the needs of a project. The article is aimed at presenting the comprehensive approach in evaluating and analyzing investment risks. Design/Methodology/Approach: When studying the use of fuzzy logic methods, it is necessary to determine the main drawbacks and limitations of the current economic and mathematical models, as well as methods for evaluating the effectiveness and risks of projects. Findings: Using fuzzy logic, authors analyzed risk categories in two key stages by examining the net present value index. Authors applied the method of risk assessment based on the integrated risk assessment V&M, forming full range of investment scenarios and determining the unacceptable risk values. Practical Implications: The authorsā approach could be applied in predicting changes in economic activity under the influence of external and internal factors. Originality/Value: The study highlights the key features of fuzzy logic methods in analyzing projects when processes are difficult to formalize, and subjective criteria exist.peer-reviewe
- ā¦