Using multithreshold quadratic sigmoidal neurons to improve classification capability of multilayer perceptrons
نویسندگان
چکیده
This letter proposes a new type of neurons called multithreshold quadratic sigmoidal neurons to improve the classification capability of multilayer neural networks. In cooperation with single-threshold quadratic sigmoidal neurons, the multithreshold quadratic sigmoidal neurons can be used to improve the classification capability of multilayer neural networks by a factor of four compared to committee machines and by a factor of two compared to the conventional sigmoidal multilayer perceptrons.
منابع مشابه
Are Rosenblatt multilayer perceptrons more powerfull than sigmoidal multilayer perceptrons? From a counter example to a general result
In the eighties the problem of the lack of an efficient algorithm to train multilayer Rosenblatt perceptrons was solved by sigmoidal neural networks and backpropagation. But should we still try to find an efficient algorithm to train multilayer hardlimit neuronal networks, a task known as a NP-Complete problem? In this work we show that this would not be a waste of time by means of a counter ex...
متن کاملEffect of nonlinear transformations on correlation between weighted sums in multilayer perceptrons
Nonlinear transformation is one of the major obstacles to analyzing the properties of multilayer perceptrons. In this letter, we prove that the correlation coefficient between two jointly Gaussian random variables decreases when each of them is transformed under continuous nonlinear transformations, which can be approximated by piecewise linear functions. When the inputs or the weights of a mul...
متن کاملSupport Vector Machine Based Facies Classification Using Seismic Attributes in an Oil Field of Iran
Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has been widely investigated through unsupervised-classification-based studies, there are few cases...
متن کاملPerformance Improvement of Multilayer Perceptrons with Increased Output Nodes per Class
Generally, we allocate one output node per class in pattern recognition applications of MLPs(multilayer perceptrons). In this paper, we propose a method to improve generalization capability of MLPs through increasing the number of output nodes per class. We verify that the proposed method decreases misclassification ratios of MLPs through a short mathematical aspect. And then, simulations of is...
متن کاملAn ]Efficient Multilayer Quadratic Perceptron for Pattern Classification and Function Approximation
Abs t rac t : W e propose an architecture of a multilayer quadratic perceptron (MLQP) that combines advantages of multilayer perceptrons(MLPs) and higher-order feedforward neural networks. The features of MLQP are in its simple structure, practical number of adjustable connection weights and powerful learning ability. I n this paper, the architecture of MLQP is described, a backpropagation lear...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- IEEE transactions on neural networks
دوره 5 3 شماره
صفحات -
تاریخ انتشار 1994