523 research outputs found
Artificial analysis of molecular marker loci linked to tree resistance response by an artificial neural network
One of the biggest challenges that software developers face is to make an accurate estimate of the project effort. Radial basis function neural networks have been used to software effort estimation in this work using NASA dataset. This paper evaluates and compares radial basis function versus a regression model. The results show that radial basis function neural network have obtained less Mean Square Error than the regression method
Classification of Data to Extract Knowledge from Neural Networks
A major drawback of artificial neural networks is their black-box character. Therefore, the rule
extraction algorithm is becoming more and more important in explaining the extracted rules from the neural
networks. In this paper, we use a method that can be used for symbolic knowledge extraction from neural
networks, once they have been trained with desired function. The basis of this method is the weights of the neural
network trained. This method allows knowledge extraction from neural networks with continuous inputs and
output as well as rule extraction. An example of the application is showed. This example is based on the
extraction of average load demand of a power plant
An Effective Method for Constructing Data Structures Solving an Array Maintenance Problem
In this paper a constructive method of data structures solving an array maintenance problem is offered.
These data structures are defined in terms of a family of digraphs which have previously been defined,
representing solutions for this problem. We present as well a prototype of the method in Haskell
Trained Neural Network Characterizing Variables for Predicting Organic Retention by Nanofiltration Membranes
Many organic compounds cause an irreversible damage to human health and the ecosystem and are
present in water resources. Among these hazard substances, phenolic compounds play an important role on the
actual contamination. Utilization of membrane technology is increasing exponentially in drinking water production
and waste water treatment. The removal of organic compounds by nanofiltration membranes is characterized not
only by molecular sieving effects but also by membrane-solute interactions. Influence of the sieving parameters
(molecular weight and molecular diameter) and the physicochemical interactions (dissociation constant and
molecular hydrophobicity) on the membrane rejection of the organic solutes were studied. The molecular
hydrophobicity is expressed as logarithm of octanol-water partition coefficient. This paper proposes a method
used that can be used for symbolic knowledge extraction from a trained neural network, once they have been
trained with the desired performance and is based on detect the more important variables in problems where
exist multicolineality among the input variables
Analysis of P-Systems under a Multiagent Systems Perspective
Membrane computing is a recent area that belongs to natural computing. This field works on
computational models based on nature's behavior to process the information. Recently, numerous models have
been developed and implemented with this purpose. P-systems are the structures which have been defined,
developed and implemented to simulate the behavior and the evolution of membrane systems which we find in
nature. What we show in this paper is an application capable to simulate the P-systems based on a multiagent
systems (MAS) technology. The main goal we want to achieve is to take advantage of the inner qualities of the
multiagent systems. This way we can analyse the proper functioning of any given p-system. When we observe a
P-system from a different perspective, we can be assured that it is a particular case of the multiagent systems.
This opens a new possibility, in the future, to always evaluate the P-systems in terms of the multiagent systems
technology
Simultaneous Control of Chaotic Systems Using RBF Networks
Chaos control is a concept that recently acquiring more attention among the research community,
concerning the fields of engineering, physics, chemistry, biology and mathematic. This paper presents a method
to simultaneous control of deterministic chaos in several nonlinear dynamical systems. A radial basis function
networks (RBFNs) has been used to control chaotic trajectories in the equilibrium points. Such neural network
improves results, avoiding those problems that appear in other control methods, being also efficient dealing with a
relatively small random dynamical noise
Aplications of Neural Networks to Find the Impact of Water in Different Berry Components in Grapes
Grape juice composition during the different stages of berry growth was compared. The analytical data
collected were used to investigate the relationships between some of the different components studied in these
berries during the ripening period.
Our goal is to study, with neural networks, the impact of water availability on Vitis vinifera L. cv. Tempranillo grape
yields and juice composition over a three-year period
Virtual Membrane Systems
Within the membrane computing research field, there are many papers about software simulations and a few about hardware implementations. In both cases, algorithms for implementing membrane systems in software and hardware that try to take advantages of massive parallelism are implemented. P-systems are parallel and non deterministic systems which simulate membranes behavior when processing information. This paper presents software techniques based on the proper utilization of virtual memory of a computer. There is a study of how much virtual memory is necessary to host a membrane model. This method improves performance in terms of time
A Multiple Radar Approach for Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar
Along with the improvement of radar technologies, Automatic Target
Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR)
has come to be an active research area. SAR/ISAR are radar techniques to
generate a two-dimensional high-resolution image of a target. Unlike other
similar experiments using Convolutional Neural Networks (CNN) to solve this
problem, we utilize an unusual approach that leads to better performance and
faster training times. Our CNN uses complex values generated by a simulation to
train the network; additionally, we utilize a multi-radar approach to increase
the accuracy of the training and testing processes, thus resulting in higher
accuracies than the other papers working on SAR/ISAR ATR. We generated our
dataset with 7 different aircraft models with a radar simulator we developed
called RadarPixel; it is a Windows GUI program implemented using Matlab and
Java programming, the simulator is capable of accurately replicating a real
SAR/ISAR configurations. Our objective is to utilize our multi-radar technique
and determine the optimal number of radars needed to detect and classify
targets.Comment: 8 pages, 9 figures, International Conference for Data Intelligence
and Security (ICDIS
Networks of Evolutionary Processors (NEP) as Decision Support Systems
This paper presents the application of Networks of Evolutionary Processors to Decision Support
Systems, precisely Knowledge-Driven DSS. Symbolic information and rule-based behavior in Networks of
Evolutionary Processors turn out to be a great tool to obtain decisions based on objects present in the network.
The non-deterministic and massive parallel way of operation results in NP-problem solving in linear time.
A working NEP example is shown
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