Comparison of BPA and LMA Methods for Takagi - Sugeno Type MIMO Neuro-Fuzzy Network to Forecast Electrical Load TIME Series

This paper describes an accelerated Backpropagation algorithm (BPA) that can be used to train the Takagi-Sugeno (TS) type multi-input multi-output (MIMO) neuro-fuzzy network efficiently. Also other method such as accelerated Levenberg-Marquardt algorithm (LMA) will be compared to BPA. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), Mean Squared Error (MSE), and also Root Mean Squared Error (RMSE), down to the desired error goal much faster than that the simple BPA or LMA. Finally, the above training algorithm is tested on neuro-fuzzy modeling and forecasting application of Electrical load time series

PREDICTION OF RECURRENCE AND MORTALITY OF ORAL TONGUE CANCER USING ARTIFICIAL NEURAL NETWORK (A case study of 5 hospitals in Finland and 1 hospital from Sao Paulo, Brazil)

Cancer is a dreadful disease that had caused the death of millions of people. It is characterized by an uncontrollable growth of cell to form lumps or masses of tissue that are known as tumour. Therefore, it is a concern to all and sundry as these tumours mostly release hormones which have negative impact on the body system. Data mining approaches, statistical methods and machine learning algorithms have been proposed for effective cancer data classification. Artificial Neural Networks (ANN) have been used in this thesis for the prediction of recurrence and mortality of oral tongue cancer in patients. Similarly, ANN was also used to examine the diagnostic and prognostic factors. This was aimed at determining which of these diagnostic and prognostics factors had influence on the prediction of recurrence and mortality of oral tongue cancer in patients. Three different ANN have been applied for the learning and testing phases. The aim was to find the most effective technique. They are Elman, Feedforward, and Layer Recurrent neural networks techniques. Elman neural network was not able to make acceptable prediction of the recurrence or the mortality of tongue cancer based on the data. In contrast, Feedforward neural network captured the relationship between the prognostic factors and correctly predicted recurrence. However, it failed to predict the mortality based on the patient's data. Layer Recurrence neural network has been very effective and successfully predicted the recurrence and the mortality of oral tongue cancer in patients. The constructed layered recurrence neural network has been used to investigate the correlation between the prognostic factors. It was found that out of 11 prognostic factors in the data sheet, it was only 5 of them that had considerable impact on the recurrence and mortality. These are grade, depth, budding, modified stage, and gender. Time in months and disease free months were also used to train the network.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Acceleration of Levenberg-Marquardt Training of Neural Networks with Variable Decay Rate

In the application of the standard Levenherg-Marquardt training process of neural networks, error oscillations are frequently observed and they usually aggravate on approaching the required accuracy. In this paper, a modified Levenberg-Marquardt method based on variable decay rate in each iteration is proposed in order to reduce such error oscillations. Through a certain variation of the decay rate, the time required for training of neural networks is cut down to less than half of that required in the standard Levenberg-Marquardt method. Several numerical examples are given to show the effectiveness of the proposed method.published_or_final_versio

Stock Marketing Prediction Using Narx Algorithm

Computational technologies have offered faster and efficient solutions to financial sector. In the financial market, the advancements in computational field have been achieved by the use of neural networks and machine learning that delivered a number of financial tools. Thus, in this thesis, we aim to predict the stock index marketing for the “Dow Jones” index by using deep learning algorithms. We propose a model based on an adaptive NARX neural network to predict the closing price of a moderately stable market. In our model, non-linear auto regressive exogenous input model inserts delays into the input as well as the output acting as memory slots thereby raising the accuracy of the prediction. Moreover, Levenberg-Marquardt algorithm has been used for training the network. The accuracy of the model is determined by the mean squared error. We also used LR model, with the same parameters as NARX, to improve the overall accuracy