This paper addresses methods of improving the fault tolerance of feedforward neural nets. The rst method is to coerce weights to have low magnitudes during the backpropagation training process, since fault tolerance is degraded by the use of high magnitude weights; at the same time, additional hidden nodes are added dynamically to the network to ensure that desired performance can be obtained. ...

Historical manuscripts are one of documents that important to be preserved because they contain a lot information, example them is script as the historical . document mostly still use handwriting in so many reserch. Currently, there research regarding preservation characters. One way can used digitization process. Digitizing’s process tanable by recognizing existing information using technology...

The focus of this study is how we can efficiently implement the neural network backpropagation algorithm on a network of computers (NOC) for concurrent execution. We assume a distributed system with heterogeneous computers and that the neural network is replicated on each computer. We propose an architecture model with efficient pattern allocation that takes into account the speed of processors...

Backpropagation (BP) adalah sebuah metode yang digunakan dalam training Neural Network (NN) untuk menentukan parameter bobot yang sesuai. Proses penentuan parameter bobot dengan menggunakan metode backpropagation sangat dipengaruhi oleh pemilihan nilai learning rate (LR)-nya. Penggunaan nilai learning rate yang kurang optimal berdampak pada waktu komputasi yang lama atau akurasi klasifikasi yan...

Neural networks are a popular tool in the area of pattern recognition. However, since a very large number of neural network architectures exist, it has not been established which one is the most efficient. In this paper we compare the performance of three neural network architectures: Kohonen’s self-organizing network, probabilistic neural network, and a modified backpropagation applied to a si...

Handle is an important property of fabrics. In this work we tried to predict the handles of some worsted fabrics by their physical properties using a backpropagation network. Also an unsupervised kohonen network was used for clustering the fabrics. Physical properties of fabrics were measured by universal test equipments and hand values of the fabrics were determined by a panel of judges consis...

Abstract This paper presents an investigation of the approximation property of neural networks with unbounded activation functions, such as the rectified linear unit (ReLU), which is the new de-facto standard of deep learning. The ReLU network can be analyzed by the ridgelet transform with respect to Lizorkin distributions. By showing three reconstruction formulas by using the Fourier slice the...

Evolutionary Algorithms (EAs) are population based algorithms, which allow for simultaneous exploration of different parts in the Pareto optimal set. This paper presents Memetic Elitist Pareto Evolutionary Algorithm of Three-Term Backpropagation Network for Classification Problems. This memetic elitist Pareto evolutionary algorithm is called METBP and used to evolve Three-term Backpropagation (...

The ability of neural networks to closely approximate unknown functions to any degree of desired accuracy has generated considerable demand for Neural Network research in Business. The attractiveness of neural network research stems from researchers’ need to approximate models within the business environment without having a priori knowledge about the true underlying function. Gradient techniqu...