This paper proposes a limited kernel associative memory, where the number of kernels is limited to a certain number. This model aims to be used on embedded systems with a small amount of storage space. The learning algorithm for the kernel associative memory is an improved version of the limited general regression neural network, which was proposed by one of the authors. In the experiments, we ...

Agile software development is now accepted as a superior alternative to conventional methods of software development, because of its inherent benefits like iterative development, rapid delivery and reduced risk. Hence, the industry must be able to efficiently estimate the effort necessary to develop projects using agile methodology. For this, different techniques like expert opinion, analogy, d...

This paper presents a new model for predicting the compressive strength of steel-confined concrete on circular concrete filled steel tube (CCFST) stub columns under axial loading condition based on Artificial Neural Networks (ANNs) by using a large wide of experimental investigations. The input parameters were selected based on past studies such as outer diameter of column, compressive strength...

ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water...

This paper presents the application of neural networks in software quality estimation using objectoriented metrics. In this paper, two kinds of investigation are performed. The first on predicting the number of defects in a class and the second on predicting the number of lines changed per class. Two neural network models are used, they are Ward neural network and General Regression neural netw...

Data mining in direct marketing aims at identifying the most promising customers to send targeted advertising. Traditionally, statistical models are used to make such a selection. The success of statistical models depends on the validity of certain assumptions about data distribution. Artificial intelligence inspired models, such as genetic algorithms and neural networks, do not need those assu...

Applications of multi-layer feed-forward artificial neural networks (ANN) to spectroscopy are reviewed. Network architecture and training algorithms are discussed. Backpropagation, the most commonly used training algorithm, is analyzed in greater detail. The following types of applications are considered: data reduction by means of neural networks, pattern recognition, multivariate regression, ...