5,564 research outputs found
The application of ANFIS prediction models for thermal error compensation on CNC machine tools
Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.
A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4 μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system
Soft computing techniques applied to finance
Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad
Forecasting and Forecast Combination in Airline Revenue Management Applications
Predicting a variable for a future point in time helps planning for unknown
future situations and is common practice in many areas such as economics, finance,
manufacturing, weather and natural sciences. This paper investigates and compares
approaches to forecasting and forecast combination that can be applied to service
industry in general and to airline industry in particular. Furthermore, possibilities to
include additionally available data like passenger-based information are discussed
Experimental set-up for investigation of fault diagnosis of a centrifugal pump
Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated
Meta-Learning Evolutionary Artificial Neural Networks
In this paper, we present MLEANN (Meta-Learning Evolutionary Artificial
Neural Network), an automatic computational framework for the adaptive
optimization of artificial neural networks wherein the neural network
architecture, activation function, connection weights; learning algorithm and
its parameters are adapted according to the problem. We explored the
performance of MLEANN and conventionally designed artificial neural networks
for function approximation problems. To evaluate the comparative performance,
we used three different well-known chaotic time series. We also present the
state of the art popular neural network learning algorithms and some
experimentation results related to convergence speed and generalization
performance. We explored the performance of backpropagation algorithm;
conjugate gradient algorithm, quasi-Newton algorithm and Levenberg-Marquardt
algorithm for the three chaotic time series. Performances of the different
learning algorithms were evaluated when the activation functions and
architecture were changed. We further present the theoretical background,
algorithm, design strategy and further demonstrate how effective and inevitable
is the proposed MLEANN framework to design a neural network, which is smaller,
faster and with a better generalization performance
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