support vector regression (svr) is a learning method based on the support vector machine (svm) that can be used for curve fitting and function estimation. in this paper, the ability of the nu-svr to predict the catalytic activity of the fischer-tropsch (ft) reaction is evaluated and the result is compared with two other prediction techniques including: multilayer perceptron (mlp) and subtractiv...

abstract: this paper represents a novel use of artificial neural networks in medical science. the proposed technique involves training a multi layer perceptron (mlp) (a kind of artificial neural network) with a bp learning algorithm to recognize a pattern for the diagnosing and prediction of five blood disorders, through the results of blood tests from h1 machine. the blood test parameters and ...

a neuro-fuzzy modeling tool (anfis) has been used to dynamically model cross flow ultrafiltration of milk. it aims to predict permeate flux and total hydraulic resistance as a function of transmembrane pressure, ph, temperature, fat, molecular weight cut off, and processing time. dynamic modeling of ultrafiltration performance of colloidal systems (such as milk) is very important for designing ...

We present a new model for text regression that seamlessly combine engineered features and character-level information through deep parallel convolution stacks, multi-layer perceptrons and multitask learning. We use these models to create the SHEF/CNN systems for the sentence-level Quality Estimation task of WMT 2017 and Emotion Intensity Analysis task of WASSA 2017. Our experiments reveal that...

One of the most important tasks of automatic tool monitoring systems for CNC-lathes is the supervision of a tool's wear. Considering the state of wear and the actual working process (e.g. rough or nish turning) it is possible to exchange a tool (or only the insert) just in time, which o ers signi cant economic advantages. This paper presents a new method to estimate two wear parameters by means...

In a search space of a multilayer perceptron having J hidden units, MLP(J), there exist flat areas called singular regions. Since singular regions cause serious stagnation of learning, a learning method to avoid them was once proposed, but was not guaranteed to find excellent solutions. Recently, SSF1.2 was proposed which utilizes singular regions to stably and successively find excellent solut...

This paper presents a system for recognizing sketched logic circuits in real-time and graphically simulating them afterwords. It has been developed for use in university and school education. Circuit gate symbols are recognized using a multilayer perceptron network. The simulation is fully controlled by hand-drawings, and the inputs to circuits can be defined by writing numbers next to them. In...

Motivated by the problem of training multilayer perceptrons in neural networks, we consider the problem of minimizing E(x) = ∑ni=1 fi(ξi · x), where ξi ∈ Rs , 1 i n, and each fi(ξi · x) is a ridge function. We show that when n is small the problem of minimizing E can be treated as one of minimizing univariate functions, and we use the gradient algorithms for minimizing E when n is moderately la...

The recognition of the emotional states of speaker is a multidisciplinary research area that has received great interest in the last years. One of the most important goals is to improve the voiced-based human-machine interactions. Recent works on this domain use the prosodic features and the spectrum characteristics of speech signal, with standard classifier methods. Furthermore, for traditiona...