{"title":"Optical Character Recognition Using Novel Feature Extraction & Neural Network Classification Techniques","authors":"B. Gatos, Dimitrios Alexios Karras, S. Perantonis","doi":"10.1109/NNAT.1993.586055","DOIUrl":"https://doi.org/10.1109/NNAT.1993.586055","url":null,"abstract":"This paper describes two novel techniques applied to the jkature extraction and pattern classification stages in an OCR system for typeset characters. A technique for estimating the class discrimination ability of continuous valued jkatures is presented leading to the formation of complex features which facilitate the classifimtion stage. Next, a neural network ClQSSafieT trained wing a nxently proposed powerfisl training algorithm, based m rigorous nonlinear programming methods, kz applied to large-scale OCR problems involving typeset Greek characters and found to exhibit good generalization capabilities compared to other conventional and artificial neural network (ANN) classifiers. Combining these jkature extraction and classification techniques in a unified software platform, we have designed an OCR system which achieved high mognition rates in some real world OCR ezperiments.","PeriodicalId":164805,"journal":{"name":"Workshop on Neural Network Applications and Tools","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124928973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. B. Larkin, E. Hines, S. M. Thomas, J. W. Gardner
{"title":"Supervised Learning Using The Vector Memory Array Method","authors":"A. B. Larkin, E. Hines, S. M. Thomas, J. W. Gardner","doi":"10.1109/NNAT.1993.586047","DOIUrl":"https://doi.org/10.1109/NNAT.1993.586047","url":null,"abstract":"The Vector Memory Array (VMA) is a novel neural network architecture. The principles of VMA are presented here and it is applied to data gathered by an Electronic nose in response to five simple odours (alcohols) and three complex odours (coffees). VMA achieved 100% accuracy on the alcohol data-set (40 samples) and 92% accuracy on the coffee data-set (90 samples) in just a few seconds. These results suggest a superior generalisation capability and learning speed compared to other neural paradigms, such as backpropagation, Alpaydin ’s constructive learning and logical neurons. Although VMA requires the assignment of the input vectors to an input hidden array layer, the associated memory cost may be offset in applications where fast processing and easy changes in training set are the principal requirements.","PeriodicalId":164805,"journal":{"name":"Workshop on Neural Network Applications and Tools","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127048273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Investigation Of The Potential Value Of Neural Network Computing In Diagnosis And Assessment Of Breast Cancer: Analysis Of Blood Plasma By 1H Nuclear Magnetic Resonance Spectroscopy","authors":"R. Maxwell, S. Howells, S. Chen, J. Griffiths","doi":"10.1109/NNAT.1993.586049","DOIUrl":"https://doi.org/10.1109/NNAT.1993.586049","url":null,"abstract":"Introduction 'H Nuclear Magnetic Resonance (WR) spectra were obtained from plasma samples from patients with breast cancer, with benign breast disease and from healthy volunteers. Preprocessing of the data included the use of principal component (PC) analysis and resulted in reduction of the data dimensions to 6-8 PC scores. A backpropogation neural network with two hidden layers was used to learn how to convert these PC scores (used as inputs) into outputs indicative of class membership. Examination of the similarity between the PC scores for these samples was performed using cluster analysis and from the weights obtainedfrom a single layer network From these results it could be predicted that, contrary to our prior expectations, it should be relatively easy to distinguish benign breast disease from the other two clwses but that there would be considerable overlap between cancer and normal subjects. fiis was also the conclusion of an assessment of the reliability of the network when c1assifLing samples that had not been included in the learning stage. Ihe best resultsfrom this study were that 85% of samples in the normal versus benign diseace datmet could be correctly assigned after having been omitted from the learning stage. i%e other 15% were designated ar unclassified since the output scores were ambiguous. Moderately good results from the most clinically interesting pair of classes, benign disease versus cancer, were improved on by incorporating information about type of treatment and secondary diseases into additional output scores. l%is approach appears to be helpful in reducing the problems associated with heterogeneity within one or more of the classes.","PeriodicalId":164805,"journal":{"name":"Workshop on Neural Network Applications and Tools","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131932477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Double Input Layered Neural Network - Using Input Weights For Better Understanding Of Decision Reasoning: A Medical Application","authors":"Z. Shen, M. Clarke, R. Jones","doi":"10.1109/NNAT.1993.586050","DOIUrl":"https://doi.org/10.1109/NNAT.1993.586050","url":null,"abstract":"An unique structure of Multi-layered Perceptron (MLP) with double input layers is proposed. By using a second input layer to a traditional MLP, a set of input weights between the two single connected input layers is obtained. We aim to use these weights to determine the contribution and significance of each input to the decision making. we also found that the learning process is accelerated when the additional layer is used. In this paper, we report our results and compare them with the traditional MLP. The significance of weight analysis is that: the contribution of each input helps explain the decision made by the network, which has been regarded as one of major disadvantages of neural networks; the weights can be used to select subsets of inputs and reduce input dimensions.","PeriodicalId":164805,"journal":{"name":"Workshop on Neural Network Applications and Tools","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132305188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Scene Analysis For Navigation Tasks And Robotics Using A Harmony Theory Network","authors":"Tatiana Tambouratzis","doi":"10.1109/NNAT.1993.586054","DOIUrl":"https://doi.org/10.1109/NNAT.1993.586054","url":null,"abstract":"This chapter describes a parallel implementation of a novel scene analysis system. The implementation is based on a Harmony Theory network, since Harmony Theory has been proved to be very competent at tasks requiring constraint propagation. The network (a) receives a thresholded projection of the viewed image (line-drawing) as its input, (b) utilises a novel system of l abelling schemes (precompiled information) for the construction of its upper layer and (c) outputs the decisions concerning the c haracterisation s of all the lines in the line-drawing (result of the network-settling ); these decisions constitute the 3-D description of the viewed scene. The rival objectives of transparency and economy in the construction are the focal points of this network. It is thus ensured that the implementation settles accurately and fast, in order to be appropriate for real applications.","PeriodicalId":164805,"journal":{"name":"Workshop on Neural Network Applications and Tools","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127522521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Development Of A Neural Network Model Based Controller For A Non-linear Process Application","authors":"J. Gomm, J. Evans, D. Williams, P.J.G. Lisboa","doi":"10.1109/NNAT.1993.586061","DOIUrl":"https://doi.org/10.1109/NNAT.1993.586061","url":null,"abstract":"Process control using a model based structure incorporating a neural network is examined by application to the control of a real pilot-scale process exhibiting non-linearities and typical disturbances. Initially, a methodology for identibing an accurate neural network process model from plant data is described and practical aspects of applying the techniques are discussed. It is shown that the approach leads to a neural network description of the process dynamics that is suficiently accurate to be used independently from the process, emulating the process response from only process input information. The main success of the approach is the use of a novel coding technique for representing data in the network. The network model is incorporated into a model predictive control structure and on-line results illustrate the improvements in control performance that can be achieved compared to conventional PI control. Additionally, an insight into the dynamics and stability of the neural control scheme is obtained in a novel application of linear system identification techniques.","PeriodicalId":164805,"journal":{"name":"Workshop on Neural Network Applications and Tools","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132881823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Neural Network For Detecting Fractals In Spatial Patterns","authors":"Bernd Freisleben, J. Greve, J. Lober","doi":"10.1109/NNAT.1993.586053","DOIUrl":"https://doi.org/10.1109/NNAT.1993.586053","url":null,"abstract":"In this paper, a neural network approach for classifying given spatial patterns into fractals or non-fractals as presented. The proposed network is a hierarchically organized, multi-layer feedforward architecture which exploits the structural properties of artificially generated fractals. The backpropagation algorithm is employed for training the network. The network is implemented in parallel on a multi-transputer system. It is able to classify all non-noisy test patterns correctly; fractal patterns where 1/ f and random noise are added take longer for the network to converge, but up to a certain signal-to-noise ratio they are also classified correctly.","PeriodicalId":164805,"journal":{"name":"Workshop on Neural Network Applications and Tools","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132596908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"State Estimation Technique And Predictive Control Based On Artificial Neural Networks","authors":"N. A. Jalel, R. Malcolm, J. R. Leigh","doi":"10.1109/NNAT.1993.586062","DOIUrl":"https://doi.org/10.1109/NNAT.1993.586062","url":null,"abstract":"Severe problems occur in the control of fermentation because of the poorly understand nature of the process, its nonlinearity and the wide range of operating states passed through during a batch. During a typical production batch, important variables such as product concentration are determined by slow infrequent 08line laboratory analysis, making this set of variables of limited use for control. In this work, the artijlcial neural network technique has been used for the on-line estimation of the important state variables of the fed batch fermentation process. The neural network tasks include both modelling and state estimation of the residual nitrogen inside the fermenter (Residual nitrogen is one of the key variables required for improved control.) The second part of the paper describes a controller design based on the predictive control approach. The aim of the controller is to maintain residual nitrogen around a desired level by controlling the amount of soluble nitrogen fed.","PeriodicalId":164805,"journal":{"name":"Workshop on Neural Network Applications and Tools","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134174745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Intelligent Gain Scheduling (igs) Using Neural Networks For Robotic Manipulators","authors":"Q. Wang, D. Broome, A. Greig","doi":"10.1109/NNAT.1993.586060","DOIUrl":"https://doi.org/10.1109/NNAT.1993.586060","url":null,"abstract":"Existing industrial robotic manipulators have proven to be limited in many applications, especially in their payloads and manipulation speeds. This paper presents an Intelligent Gain Scheduling control scheme using neural networks. It advances the idea of mapping the non-linear relationship between robot working conditions (e.g. payload, speed, etc.) and its controller’s gains. The aim of this research is to try to propose an applied robot controller, which is not too expensive, is acceptable to industry and can largely improve the pe~omance of existing robot manipulators. Simulation has shown promising results.","PeriodicalId":164805,"journal":{"name":"Workshop on Neural Network Applications and Tools","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115600581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using Kohonen Feature Maps To Monitor The Condition Of Synchronous Generators","authors":"H. Jiang, J. Penman","doi":"10.1109/NNAT.1993.586058","DOIUrl":"https://doi.org/10.1109/NNAT.1993.586058","url":null,"abstract":"This paper illustrates the way in which a neural network, employing unsupervised learning, can be used for the automatic surveillance of the operational condition of synchronous generators. Results show that, if the size of the network is chosen judiciously, then it is possible to cosistently, and unambiguously identifi a range of atypical conditions in such machines.","PeriodicalId":164805,"journal":{"name":"Workshop on Neural Network Applications and Tools","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114547521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}