PERBANDINGAN PERFORMA PREDIKSI TINGKAT KEMISKINAN ANTARA BACKPROPAGATION NEURAL NETWORK DAN GENERALIZED REGRESSION NEURAL NETWORK

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Backpropagation Neural Network Tutorial

The Architecture of BPNN’s A population P of objects that are similar but not identical allows P to be partitioned into a set of K groups, or classes, whereby the objects within the same class are more similar and the objects between classes are more dissimilar. The objects have N attributes (called properties or features) that can be measured (observed) so that each object can be represented b...

متن کامل

A generalized ABFT technique using a fault tolerant neural network

In this paper we first show that standard BP algorithm cannot yeild to a uniform information distribution over the neural network architecture. A measure of sensitivity is defined to evaluate fault tolerance of neural network and then we show that the sensitivity of a link is closely related to the amount of information passes through it. Based on this assumption, we prove that the distribu...

متن کامل

Neural Network Model of the Backpropagation Algorithm

We apply a neural network to model neural network learning algorithm itself. The process of weights updating in neural network is observed and stored into file. Later, this data is used to train another network, which then will be able to train neural networks by imitating the trained algorithm. We use backpropagation algorithm for both, for training, and for sampling the training process. We i...

متن کامل

Reinforced backpropagation for deep neural network learning

Standard error backpropagation is used in almost all modern deep network training. However, it typically suffers from proliferation of saddle points in high-dimensional parameter space. Therefore, it is highly desirable to design an efficient algorithm to escape from these saddle points and reach a good parameter region of better generalization capabilities, especially based on rough insights a...

متن کامل

Learning Neural Network Architectures using Backpropagation

Deep neural networks with millions of parameters are at the heart of many state of the art machine learning models today. However, recent works have shown that models with much smaller number of parameters can also perform just as well. In this work, we introduce the problem of architecture-learning, i.e; learning the architecture of a neural network along with weights. We start with a large ne...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Jurnal Technopreneur (JTech)

سال: 2018

ISSN: 2548-558X,2252-4002

DOI: 10.30869/jtech.v6i2.210