Backpropagation Algorithms

Share Market is an untidy place for predicting since there are no significant rules to estimate or predict the price of share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc are all used to attempt to predict the price in the share market but none of these methods are proved as a consistently acceptable prediction tool. Artificial Neural Network (ANN), a field of Artificial Intelligence (AI), is a popular way to identify unknown and hidden patterns in data which is suitable for share market prediction. For predicting of share price using ANN, there are two modules, one is training session and other is predicting price based on previously trained data. We used Backpropagation algorithm for training session and Multilayer Feedforward network as a network model for predicting price. In this paper, we introduce a method which can predict share market price using

In this paper, a neurofuzzy filter (NFF) is presented,
which is based on fuzzy if-then rules (structure learning) and the
tuning of the parameters of membership function (parameter
learning). In the structure learning, fuzzy rules are found based
on the matching of input-output cluster and in parameter
learning, fine tuning is obtained for output accuracy of a network.
To minimize the output error, a new kind of backpropagation
algorithm is developed. In this algorithm we propose a variational
method for determining globally optimal learning parameters
and learning rules for on-line gradient descent training of
multilayer neural network. Both structure and parameter learning
are performed simultaneously as the adaptation proceeds.
Simulations in hand free mobile & echo cancellation on a
transmission line are performed. Noise cancellation of hand free
mobile by the proposed NFF is illustrated and good performance
is achieved with root mean square error (RMSE) of 0.0440 and
maximum instant error of 0.02.Also good performance is achieved
by applying the NFF to echo cancellation on a transmission
line.

Artificial Neural Networks (ANNs) have attracted increasing attention from researchers in many fields, including economics, medicine and computer processing, and have been used to solve a wide range of problems. In remote sensing research, ANN classifiers have been used for many investigations such
as land cover mapping, image compression, geological mapping, and meteorological image classification, and have generally proved to be more powerful than conventional statistical techniques, especially when the training data are not normally distributed. The use of ANNs requires some critical
decisions on the part of the user, which may affect the accuracy of the resulting classification. In this study, determination of the optimum network structure, which is one of the most important attributes of a network, is investigated. The structure of the network has a direct effect on training time and classification
accuracy. Although there is some discussion in the literature of the impact of network structure on the performance of the network, there is no certain method or approach to determine the best structure. Investigations of the relationship between the network structure and the accuracy of the classification
are reported here, using a MATLAB tool-kit to take the advantage of scientific visualisation. The effect of the composition of the training data on network structure is also investigated.

ABSTRAK Curah hujan adalah informasi penting di bidang transportasi, pertanian, industri dll. Dengan mengetahui informasi curah hujan, tindakan dapat diambil secara tepat di beberapa bidang tersebut. sehingga tidak ada kerugian karena kesalahan dalam informasi curah hujan. Makalah ini bertujuan untuk menemukan metode yang sesuai dalam peramalan curah hujan yang terkait dengan metode pemrosesan data imputasi dan nilai parameter dalam Support Vector Regression (SVR). Hasil percobaan menunjukkan bahwa metode preprocessing data imputasi terbaik diperoleh untuk digunakan ke dalam SVR berdasarkan nilai Mean Squared Error (MSE) dan Mean Absolute Error (MAE). Berdasarkan hasil MSE, k-nearest neighbor adalah metode terbaik yang digunakan untuk preprocessing data imputasi. Data preprocessing menghasilkan eksperimen pada SVR Polinomial dengan parameter C 1000, toleransi 0,001, epsilon 0,01 dan iterasi tak terbatas. Di sisi lain, hasil MAE menunjukkan bahwa Artificial Neural Network (ANN) adalah metode terbaik dalam imputasi data preprocessing. ANN dengan radial basis function kernel, gamma 0,001, C 1000, toleransi 0,001 dan iterasi tanpa batas. JST diuji pada RBF SVR dengan gamma 0,001, C 1000, toleransi 0,001 dan iterasi tak terbatas. Kata kunci: curah hujan; imputasi data; support vector regression; k-nearest neighbor; radial basis function kernel; artificial neural network. ABSTRACT Rainfall is important information in the fields of transportation, agriculture, industry etc. By knowing rainfall information, action can be taken precisely in some of these fields so that there is no loss due to errors in rainfall information. This paper aims to find a suitable method in forecasting rainfall related to the data imputation preprocessing method and parameter values in the Support Vector Regression (SVR). The experiment results showed that the best imputation data preprocessing method obtained for use into SVR based on Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Based on the results of MSE, k-nearest neighbor is the best method used for preprocessing imputation data. The preprocessing data results experiment on the Polynomial SVR with parameters C 1000, tolerance 0.001, epsilon 0.01 and infinite iterations. On the other hand, the MAE results show that Artificial Neural Network (ANN) is the best method in imputation preprocessing data. ANN with radial basis function kernel, gamma 0.001, C 1000, tolerance 0.001 and infinite iterations. ANN was tested on RBF SVR with gamma 0.001, C 1000, tolerance 0.001 and infinite iterations.

Cuaca merupakan kondisi fisis atmosfer dalam kurun waktu yang singkat. Kondisi ini selalu berubah-ubah secara tidak beraturan dalam kurun waktu tertentu, Secara geografis wilayah Indonesia sangat kaya akan curah hujan karena memiliki daerah perairan yang sangat luas sehingga penguapan sangat tinggi di Indonesia. Jaringan saraf tiruan menyediakan metodologi yang sangat handal dalam pemecahan masalah non-linier salah satunya adalah prediksi curah hujan. Pada penelitian ini penulis akan menganalisis pengaruh variasi nilai momentum pada proses prediksi curah hujan di kota medan dengan metode backpropagation neural network. Dari hasil penelitan dapat disimpulkan beberapa hal antara lain : Semakin kecil target error, maka jumlah iterasi akan semakin besar. Semakin kecil target error, maka nilai keakurasiannya cenderung semakin baik (semakin besar). Jumlah iterasi pada proses trainning dengan variasi nilai momentum lebih kecil dibandingkan dengan tanpa variasi nilai momentum. Kenaikan nilai momentum, cenderung membuat jumlah iterasi semakin kecil, namun tingkat keakurasiannya tetap bervariasi. Nilai iterasi akan semakin baik saat nilai momentum 0,4 sampai dengan 0,9. Tingkat akurasi tertinggi dicapai pada target error 0.008 dengan nilai momentum yaitu 44.97%.

Kata Kunci: momentum, prediksi curah hujan , backpropagation, neural network

Share Market is an untidy place for predicting since there are no significant rules to estimate or predict the price of share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc are all used to attempt to predict the price in the share market but none of these methods are proved as a consistently acceptable prediction tool. Artificial Neural Network (ANN), a field of Artificial Intelligence (AI), is a popular way to identify unknown and hidden patterns in data which is suitable for share market prediction. For predicting of share price using ANN, there are two modules, one is training session and other is predicting price based on previously trained data. We used Backpropagation algorithm for training session and Multilayer Feedforward network as a network model for predicting price. In this paper, we introduce a method which can predict share market price using Backpropagation algorithm and Multilayer Feedforward network.

In this paper, architecture of a neuro-fuzzy
integrated system is presented. A new kind of error
backpropagation algorithm to adjust the membership
functions of each variable and optimize fuzzy rules is
developed. To minimize the output error, a variational
method for determining globally optimal learning
parameters and learning rules for online gradient
descent training of multilayer neural network has been
proposed. Control parameters of the microwave oven
by the proposed method are illustrated and good
performance is achieved.

Backpropagation calculates an error signal for the output neurons and propagates it to the hidden neurons. Contrastive learning involves tightening the output neurons to desired values and allowing the effect to spread through feedback links throughout the network. To investigate the relationship between these two forms of learning, we consider a special case in which they are identical: a multilayer percep-tron with linear output units to which weak feedback links have been added. Let Ω″>π. The goal of the present article is to characterize sub-geometric numbers. We show that p ̂≠1. In this context, the results of  are highly relevant. Is it possible to examine pairwise finite vectors?

Share Market is an untidy place for predicting since there are no significant rules to estimate or predict the price of share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc are all used to attempt to predict the price in the share market but none of these methods are proved as a consistently acceptable prediction tool. Artificial Neural Network (ANN), a field of Artificial Intelligence (AI), is a popular way to identify unknown and hidden patterns in data which is suitable for share market prediction. For predicting of share price using ANN, there are two modules, one is training session and other is predicting price based on previously trained data. We used Backpropagation algorithm for training session and Multilayer Feedforward network as a network model for predicting price. In this paper, we introduce a method which can predict share market price using Backpropagation algorithm and Multilayer Feedforward network.

There is multiple databases contain datasets of TP53 gene and its tumor protein P53 which believed to be involved in over 50% of human cancers cases, these databases are rich as datasets covered all mutations caused diseases (cancers), but they haven't efficient mining method can classify and diagnosis mutations patient's then predict the cancer of that patient. This paper proposed a novel mining of cancer via mutations because there is no mining method before offers friendly, effective and flexible predict or diagnosis of cancers via using whole common database of TP53 gene (tumor protein P53) as dataset and selecting a minimum number of fields in training and testing quick propagation algorithm which supporting this miming method. Simulating quick propagation network for the train dataset shows results the Correlation (0.9999), R-squared (0.9998) and mean of Absolute Relative Error (0.0029), while the training for the ALL datasets (train, test and validation dataset) have ...