Neurofuzzy prediction thesis

The buy and sell transactions are executed whenever two consecutive positions have been switched, and profit can be obtained if positions switch at the right time and the right price. Instead of the predication accuracy, the wealth growth objective function was defined for evaluating the agent performance. The objective function measures the reward obtained by the agent, it also considers the transaction fee that is an important and has a significant impact on the investment return.

In the experiment, the fuzzy-classifier proposed by Shigeo and the back-propagation neural network BNN were employed and compared their performance on forecasting by running a paper trade simulation, fuzzy-classifier is a hybrid learning approach that combines clustering, fuzzy logic and feedforward network that able to train automatically.

"Epileptic Seizure Detection And Prediction From Electroencephalogram U" by Ahmed Fazle Rabbi

Since the agent output is the advised position of security at next day, which implies that the training data had also to be labeled with positions, and base on these positions, the agent should be able to carry out a profitable reward at least in the training period. With the given security price series, the positions for training were generated automatically that in order to maximize the objective function, and a customized genetic algorithm GA was implemented for solving the optimization problem for generating the training data.

The experiment sample was the security price series of HSBC Holding Limited, the corresponding technical indicator and other data in different categories were collected for supervised learning. In order to reduce the number of input dimensions, the RELIEF and contextual merit, and correlation analysis were taken for feature selection and discarded the duplication inputs.


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  • 1. Introduction!
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Prediction of Enrollment using Computational Intelligence. Ryan Stallings , Georgia Southern University. This work presents a study on prediction of university enrollment using three computational intelligence CI techniques.

International Journal of Computational Intelligence Systems

The enrollment prediction has been considered as a form of time series prediction using CI techniques that include an artificial neural network ANN , a neurofuzzy inference system ANFIS and an aggregated fuzzy time series model. The third technique is based on an aggregated fuzzy time series model that utilizes both global trend of the past data and the local fuzzy fluctuations. The first two CI models have been developed for one-step-ahead prediction of time series using the data of the current time and three previous time steps.

These are highly dependent on an accurate short-term load forecasting. One the other hand, the load prediction is also of great importance for energy suppliers, independent system operators, financial institutes and other parties involved in power generation, transmission, distribution and market.

matindoor.com/modules/sequtipa/logiciel-detection-logiciel-espion.php For the energy suppliers, a timely implementation of long-term load forecasting helps the system to improve the network stability and reduce the equipment failures and power outages and ensures a reliable energy supply. This dissertation have tried to propose a new approach toward the problem of load forecasting to provide more accurate results with a lesser error rate compared to the current methods.

After a short introduction on the chaos theory and nonlinear systems in the second chapter, the existence of chaos in the electrical load of the Clausthaler Umwelttechnik Forschungszentrum CUTEC in the year as the sample data is examined by three different methods. This chapter also deals with the concept drift as a kind of anomaly existence in time series, which can help to classify the input data based on the similarities and prepare them for a systematic feeding in the training process.

The third chapter will briefly provide the basic information on electricity load prediction in general, introducing the classic and modern approaches toward the problem of load forecasting. The fourth chapter describes a tool for analyzing the use of contextual information by fuzzy neural network.