NeuroSolutions for Excel is a Microsoft Excel add-in that simplifies and enhances the process of getting data into and out of a NeuroSolutions neural network. This tool benefits both the novice and the advanced neural network developer by offering easy to use, yet extremely powerful features. The foremost feature of this product is that all tasks can be performed directly from Excel!
The Custom Solution Wizard does more than just create an isolated DLL. It integrates the DLL into a working sample application, giving you an excellent starting point for your own application. Sample applications can be created for Visual Basic, Access, Excel, Visual C++ and Active Server Pages (ASP web pages). In addition, you can also use the CSW to develop custom neural network models for our financial analysis product, TradingSolutions.
The NeuroSolutions for MATLAB neural network toolbox is avaluable addition to MATLAB's technical computing capabilities allowing users to leverage the power of NeuroSolutions inside MATLAB. The toolbox features 15 neural models, 5 learning algorithms and a host of useful utilities integrated in an easy-to-use interface, which requires "next to no knowledge" of neural networks to begin using the product.
“Introduction to Neural Network:A practical approach with NeuroSolutions” is a one-day hands-on workshop that focus on fundamental concepts and techniques for analysis and design of neural computation as an approach to intelligent problem solving. A great feature of the course is that the teaching material will illustrate practical graphical neural network development tools (NeuroSolutions) that enable you to easily create a neural networks model from your data. The course also illustrate the process of building of neural network directly from Excel that simplifies and enhances the process of getting data into and out of a neural network.
TradingSolutions is a software product that helps you make better trading decisions by combining traditional technical analysis with state-of-the-art artificial intelligence technologies. Use any combination of financial indicators in conjunction with advanced neural networks and genetic algorithms to create trading models that are remarkably effective.


Application Summaries

Electronic Nose and Neural Network use
for the Classification of Honey

Simona BENEDETTIa*, Saverio MANNINOa, Anna Gloria SABATINIb, Gian Luigi MARCAZZANb


Seventy samples of honey of different geographical and botanical origin were analysed with an electronic nose. The instrument, equipped with 10 Metal Oxide Semiconductor Field Effect Transistors (MOSFET) and 12 Metal Oxide Semiconductor (MOS) sensors, was used to generate a pattern of the volatile compounds present in the honey samples. The sensor responses were evaluated by Principal Component Analysis (PCA) and Artificial Neural Network (ANN). Good results were obtained in the classification of honey samples by using a neural network model based on a multilayer perceptron that learned using a backpropagation algorithm. The methodology is simple, rapid and results suggest that the electronic nose could be a useful tool for the characterisation and control of honey.

NOTE: This is just an abstract of the application summary. The entire summary is available for download in Word format by clicking here.


Noninvasive Method of Assessing Power of Breathing (POB) for Patients Receiving Pressure Support Ventilation
M.J. Banner PhD, N.R. Euliano PhD, P. Blanch RRT, A. Gabrielli MD
University of Florida, College of Medicine, Departments of Anesthesiology, and Physiology, and NeuroDimension, Inc., Gainesville, Florida, USA
* Study supported by a grant from the National Science Foundation

A primary goal of mechanical ventilatory support is reduction of excessive work of breathing (WOB) per minute or POB. POB, the rate at which work of breathing is done, is a better assessment of respiratory muscle workloads than work of breathing because POB reflects the spontaneous inspiratory effort over time, not for an individual breath. Pressure support ventilation (PSV) is commonly used to assist spontaneous inhalation to maintain POB in a tolerable range (respiratory muscle load tolerance concept). Although appealing for directly assessing respiratory muscle workloads, measurement of POB on a routine basis is impractical due to the need to insert a balloon catheter to measure intraesophageal pressure (Pes). Other factors limiting the practicality of this approach are added equipment costs, and training to use these devices. We hypothesize that by using an artificial neural network (ANN) 5 with appropriate physiologic predictor variables, real-time POB data can be predicted accurately and noninvasively, without inserting a Pes catheter.

NOTE: The above is an just an introduction. The entire paper is available for download in PDF format by clicking here.


Predicting Beer Flavours From Chemical Analysis
C.I. Wilson & L.Threapleton
Coors Brewers, Technical Centre, P.O Box 12, Cross Street, Burton-on-Trent, DE14 1XH, UK

We all work in an industry where the consumer is king. We are constantly trying to evolve our products to satisfy the consumer changing requirements whilst at the same time always looking for the opportunity to develop niche products for new markets. However the relationship between beer flavour and its chemical analysis is poorly understood. Should it prove possible to predict final beer flavours according to their chemical composition, then it would open up the possibility of 'tuning' such products to meet the expectations of the consumer. The challenge is an Beer Flavour Be Predicted From Analytical Results ??

Substantial empirical data exists, in disparate data sources, concerning product chemical and sensory analysis. However, currently there is no mechanism for linking them to each other. Any such relationships are undoubtedly complex and highly non-linear. In order to identify such relationships we have turned our attention to the modern techniques of artificial intelligence, and specifically neural networks and genetic algorithms. The former is associated with machine learning whilst the latter is associated with biological evolution. The development of both these fields can be traced back to the 1960s. However it is only recently, with the rapid expansion in computing power combined with the availability of packaged software solutions that these techniques have been moved from the computer science laboratory into industry. NOTE: The above is an abstract to a paper presented at the 29th European Brewing Congress in Dublin. The entire paper is available for download in PDF format by clicking here.

An AI Method to improve Fault Analysis in Local Access Networks
Rigg S., Tindle J., Brewis S. Proceedings Volume 2
World Multiconference on Systems, Cybernetics and Informatics and Systems, Cybernetics and Informatics, SCI'98
4th International Conference on Information Systems, Analysis and Synthesis, ISAS'98 July 12-16, 1998, Orlando, Florida USA.
Organized by International Institute of Informatics and Systems, IIIS
ISBN 980-07-5079-7, Copyright 1998 IIIS
The application of IT technologies that make use of the telephone network is dramatically increasing. There is a growing demand to rapidly detect and repair faulty telecommunication lines. This paper identifies a method to anticipate and localize possible fault areas within the Local Access Copper Networks (LACNs). The system employs a proactive approach to identify potential faults within the network. Therefore it is possible to replace plant before an actual fault state occurs. The paper outlines a typical network topology, overview of artificial neural networks, current anticipation and localization procedures and suggests an artificial intelligent technique for a new anticipation and localization method. Ultimately these methods may lead to tools being developed to aid an expert and provide better guidance for field maintenance engineers to replace faulty plant.

NOTE: This is just an abstract of the application summary. The entire summary is available for download in PDF format by clicking here.


Neural Network Based Power Plant Coal Quality Analysis
H. Salehfar
Department of Electrical Engineering
University of North Dakota
Grand Forks, ND 58202

S. A. Benson
Microbeam Technologies Inc.
P. O. Box 14758
Grand Forks, ND 58208

Ash problems in coal-fired power plants result in decreases in efficiency, unscheduled outages, equipment failures, and cleaning. Assessing the potential impact of ash on power plant performance is extremely complex and difficult due to coal variability, the complexity of the ash behavior processes involved, and changing operating conditions. To predict the impact of ash on power plant performance, the impurities and mineral contents of coal have to be determined. Current coal quality evaluation methods are either inefficient or very expensive and time consuming. This paper develops a neural network which quickly determines the impurities and ash forming species in coal. The results are compared with those from computer-controlled scanning electron microscopy (CCSEM) methods. The developed model shows promise and has the potential to save coal-fired utilities millions of dollars in dealing with various coal ash problems.

NOTE: This is just an abstract of the application summary. The entire summary is available for download in PDF format by clicking here.


Seabed Recognition Using Neural Networks
Vladan Babovic
Danish Hydraulic Institute

Side Scan Sonar (SSS) imaging is one of the advanced methods for data acquisition about the sea floor. A skilled technician can interpret the images of the area surveyed and produce a base map showing the distribution different classes of seabed materials. Continued monitoring of the seabed by SSS enables the detection of changes in the seafloor. The possibilities of intelligence-based approaches in the analysis of sonar images and classification of seabed material have been explored in this study.

The only available type of measurement for classification SSS images is the grey level of the pixels corresponding to the acoustic reflectance. It is difficult to recognise and classify objects based on a single feature. However, the spatial order of the grey level transitions gives ‘texture’ characteristics to the image and it is these that act as an important aid in human interpretation. Image texture can be characterised by the Spatial Grey Level Dependence Method (SGLDM) based on the cooccurrence matrix of pairs of grey levels.

Artificial neural networks (ANNs) are powerful tool for classification problems. An ANN can learn the classification task from a set of examples known as training set. Multi Layer Perceptron (MLP) is one of the most popular supervised learning ANN models which is frequently used for classification problems. However, when the prior knowledge about the classes in the data is limited, it is difficult to prepare a data set for training the neural network classifier. Only practical alternative in such situations is to use a data exploration tool that can detect the groups in the data based on their prominent features.

The Self Organising Feature Maps (SOM) is one of the neural network models offering great potential in data exploration. The SOM algorithm is based on unsupervised competitive learning, which means that the training is entirely data driven. The various benefits of SOM include topology-preserving projection of the higher dimensional data space on a regular two-dimensional grid and clustering.

In this study, data exploration of sonar image segments has been carried out using SOM. The developed seabed recognition system consists of a tool for feature extraction from sonar images and two neural network classifiers, the labeled SOM feature map and SOM_MLP classifier. The system identifies the seabed materials like clay/mud, sand, eel grass and gravel from images using five selected features of the image segments; median, 3rd quartile, energy, entropy and momentum.

Both SOM and MLP were developed using NeuroSolutions 3.01

NOTE: This is just an abstract of the application summary. The entire summary is available for download in PDF format by clicking here.


Using Neural Networks to Distinguish Kinematics
in Use Wear Analysis


Problem Description

Archaeologists studying lithic remains usually wish to determine whether or not these stones have been used as tools and how they were used. The best way to do this is through the analysis of macro- and microscopic traces of wear generated by the use of the tool.

NOTE: The above is an introduction to the application summary. The entire summary is available for download in PDF format by clicking here.

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