Neurocomputing Techniques Research Articles

Evolving neurocomputing systems for horticulture applications

This paper describes the use of an advanced neurocomputing technique and a novel knowledge engineering methodology for knowledge acquisition, manipulation, and extraction of data that is directly related to New Zealand's horticulture industry and furthers this area by introducing a novel and innovative knowledge-based architecture for knowledge discovery using an on-line/real-time incremental learning system. The emphases of this work highlights knowledge discovered from these data sets and also allows for greater insight into what aspects of the collected data contribute to the development of high quality produce.

Weather analysis using ensemble of connectionist learning paradigms

This paper presents a comparative analysis of different connectionist and statistical models for forecasting the weather of Vancouver, Canada. For developing the models, one year's data comprising of daily temperature and wind speed were used. A multi-layered perceptron network (MLPN) and an Elman recurrent neural network (ERNN) were trained using the one-step-secant and Levenberg–Marquardt algorithm. Radial basis function network (RBFN) was employed as an alternative to examine its applicability for weather forecasting. To ensure the effectiveness of neurocomputing techniques, the connectionist models were trained and tested using different datasets. Moreover, ensembles of the neural networks were generated by combining the MLPN, ERNN and RBFN using arithmetic mean and weighted average methods. Subsequently, performance of the connectionist models and their ensembles were compared with a well-established statistical technique. Experimental results obtained have shown RBFN produced the most accurate forecast model compared to ERNN and MLPN. Overall, the proposed ensemble approach produced the most accurate forecast, while the statistical model was relatively less accurate for the weather forecasting problem considered.

Intelligent weather monitoring systems using connectionist models

This paper presents a comparative study of different neural network models for forecasting the weather of Vancouver, British Columbia, Canada. For developing the models, we used one years data comprising of daily maximum and minimum temperature, and wind-speed. We used Multi-Layered Perceptron (MLP) and an Elman Recurrent Neural Network (ERNN), which were trained using the one-step-secant and Levenberg-Marquardt algorithms. To ensure the effectiveness of neurocomputing techniques, we also tested the different connectionist models using a different training and test data set. Our goal is to develop an accurate and reliable predictive model for weather analysis. Radial Basis Function Network (RBFN) exhibits a good universal approximation capability and high learning convergence rate of weights in the hidden and output layers. Experimental results obtained have shown RBFN produced the most accurate forecast model as compared to ERNN and MLP networks.

Neuro-computing versus linear statistical techniques applied to liver transplant monitoring: a comparative study.

This paper explores the potential for the application of neurocomputing in on-line monitoring in the liver transplantation domain. It extends our previously documented work to provide both an assessment of the performance gains achievable by incorporating temporal and dynamical information about the measurements made on a patient as well as presenting a novel computerized clinical decision aid for this domain. A comparison of the performance of linear and nonlinear classification system is made and used to motivate the final selection of the diagnostic inputs.

Destination Portfolios Using a European Vacation Style Typology

Two concepts are discussed in this study: (1) an a posteriori segmentation with psychographic variables and (2) customer loyalty in tourism. It tries to overcome the insufficiency of lifestyle segmentation detected in explaining tourism behaviour by exploiting the advantage of the multivariate nature of combined motivational and behavioural patterns of tourism demand. The segmentation task exercised with the neurocomputing technique of self-organising feature maps (SOM) resulted in a European vacation style typology of nine different prototypes. When merging two samples of vacationers, a travel survey in five generating countries and the National Austrian Guest Survey, by means of the condensed psychographic profiles it is possible to generate synergy in the process of data analysis. Both data sets shed light on Austria as a tourism destination from different perspectives. When exploiting the information that refers to loyalty and destination preferences there is no barrier to break up the corset of country-by-country analysis and to switch to the level of psychographic target segments. The approach of directional policy matrix is applied by defining qualitative measures for market attractiveness and competitive position. The strategic implication of different portfolio positions are discussed.

Applications of neurocomputing in traffic management of ATM networks

In the near future, high speed integrated networks, employing asynchronous transfer mode (ATM) cell switching and multiplexing technique, will be used to provide new and diverse mixture of services and applications. Multimedia teleconferencing, video-on-demand, television broadcasting, and distant learning are some examples of these emerging services. The ATM technique is based on the principle of statistical multiplexing, which is flexible enough to support different types of traffic while providing efficient utilization of the network's resources. New classes of techniques such as neural networks and fuzzy logic have many adaptive, learning and computational capabilities that can be utilized to design effective traffic management algorithms. The subject of this paper is to demonstrate how such neurocomputing techniques can be used to address ATM traffic management issues such as traffic characterization, call admission control, usage parameters control and feedback congestion control.

Model-free forecasting for nonlinear time series (with application to exchange rates)

The first step in nonlinear model construction is data identification. While the major drawback with most of the nonlinear tests is that they are designed to test a parametric null against certain parametric alternatives and are therefore inconsistent against all possibile alternatives. In this paper, by incorporating the neurocomputing technique, the intriguing model construction and forecast problems such as how to find a best fitted model and to obtain a more appropriate prediction performance are addressed. The dynamic abilities of this approach lies in the fact that the multilier feed forward networks are functional approximators for any functions. Simulations verify that the neural networks perform a robust forecast for some nonlinear time series. Finally, we use the data of the exchange rates of Taiwan to compare the predictive performances of neural networks with ARIMA models.

ID number recognition of X-ray films by a neural network.

Results of ID number recognition of X-ray films using the neuro-computing technique implemented on a conventional engineering workstation are reported. The neural network was trained to identify the ten digits of arabic numerals and two roman letters printed on X-ray films. We used 22 sheets of films for the training set and 23 sheets for the test set. Each sheet of film contains 25 characters. A 99.5% recognition rate on the character base was achieved.

MODELING SENSORY COLOR QUALITY OF TOMATO AND PEACH: NEURAL NETWORKS AND STATISTICAL REGRESSION

ABSTRACT In this study, neuro-computing and statistical regression techniques were applied to the determination of mathematical relationships linking human sensory judgments to physical measurements of external color for tomato and peach. Both techniques provided the same answers regarding the selection of the factors to be included in the final mathematical equations. Statistical techniques did have a slight advantage in numerical accuracy as shown by paired-comparison t-tests, but neuro-computing techniques involved less steps during the analysis phase. Thus, a compromise recommendation is to use neural networks to get the basic features of the linkages, then to use statistical techniques to fine tune the numerical accuracy of resulting mathematical functions. Relationships between sensory evaluations and physical measurements of tomato and peach external color were found to be mostly linear.