Analytische Beschreibung von Ereignisabhängigkeiten in neuronalen Systemen

Die Erregungsausbreitung in neuronalen Systemen beruht auf materieller Grundlage, Transmittermoleküle werden präsynaptisch emittiert und postsynaptisch absorbiert. Emission und Absorption sind einander sich selbst verursachende Prozesse, sie sind voneinander ereignisabhängig und damit nur schwer zu unterscheiden. Diese Schwierigkeit wird prekär, wenn es darum geht, den Prozeß der Erregungsausbreitung technisch modellieren und simulieren zu wollen. Im Verlaufe der Simulation bilden sich Abhängigkeiten heraus, deren Ursachen nicht mehr vereinzelt werden können. Demzufolge ist es schwierig, das Verhalten des Simulationsmodells zu prognostizieren. Gleichermaßen schwierig ist es aber auch, das gezeigte Verhalten zweifelsfrei interpretieren zu wollen. Aus diesem Grunde macht es sich erforderlich, das Verhalten eines neuronalen Netzes auf analytischem Wege zu beschreiben. Erschwerend wirkt hierbei der Umstand, daß es innerhalb des Netzes voneinander ereignisabhängige Prozesse gibt, die sich selbst verursachen. Zur Beschreibung dessen gibt es zwei in Raum und Zeit variable Parameter: erstens die Vorzugsorientierung bei der Erregungsausbreitung, bezeichnet als 'Beweglichkeit', und zweitens die Durchlässigkeit des Netzes für den Erregungstransport, bezeichnet als 'Diffusionskoeffizient'. Diese beiden Parameter werden hergenommen, um eine vektoranalytische Beschreibungsgleichung abzuleiten, Unterschiede zu 'klassischen' neuronalen Netzen werden herausgestellt

Connectionist methods for classification of fruit populations based on visible-near infrared spectrophotometry data

Please note that this is a searchable PDF derived via optical character recognition (OCR) from the original source document. As the OCR process is never 100% perfect, there may be some discrepancies between the document image and the underlying text.Variation in fruit maturation can influence harvest timing and duration, post-harvest fruit attributes and consumer acceptability. Present methods of managing and identifying lines of fruit with specific attributes both in commercial fruit production systems and breeding programs are limited by a lack of suitable tools to characterise fruit attributes at different stages of development in order to predict fruit behaviour at harvest, during storage or in relation to consumer acceptance. With visible-near infrared (VNIR) reflectance spectroscopy a vast array of analytical information is collected rapidly with a minimum of sample pre-treatment. VNIR spectra contain information about the amount and the composition of constituents within fruit. This information can be obtained from intact fruit at different stage of development. Spectroscopic data is processed using chemometrics techniques such as principal component analysis (PCA), discriminant analysis and/or connectionist approaches in order to extract qualitative and quantitative information for classification and predictive purposes. In this paper, we will illustrate the effectiveness of a model, connectionist and hybrid approaches, for fruit quality classification problems.Unpublished[1] J.G. Cruz-Castillo, S. Ganeshanandam, B.R. MacKay, G.S. Lewes, C.R.O. Lawoko, and D.J. Woolley, “Applications of canonical discriminant analysis in horticultural research,” HortScience, pp. 1115-1119, 1994. [2] P. Geladi and E. Dåbakk, “An overview of chemometrics applications in near infrared spectrometry,” J. Near Infrared Spectrosc., Vol. 3, pp. 119-132, 1995. [3] M. Hana, W.F. McClure, and T.B. Whitaker, Applying artificial neural networks. I. Estimating nicotine in tobacco from near infrared data,” J. Near Infrared Spectrosc., Vol. 3, pp. 133-142, 1995. [4] W. Hauptmann, and K. Heesche, “A Neural Net Topology for Bidirectional Fuzzy-Neuro Transformation,” Proc. of the Fuzz-IEEE/IFES, Yokohama, Japan, pp. 1511-1518, 1995. [5] C. Hervás, A. Garrido, B. Lucena, N. García, and E. De Pedro, “Near infrared spectroscopy for classification of Iberian pig carcasses using an artificial neural network.” J. Near Infrared Spectrosc., Vol. 2, pp. 177-184, 1994. [6] M. Ishikawa, “Structural Learning with Forgetting,” Neural Networks, Vol. 9, No. 3, pp. 509-521, 1996. [7] J.-S. Roger Jang, “ANFIS: Adaptive-network-based fuzzy inference systems,” IEEE Trans. on System, Man and Cybernetics, Vol. 23, No. 3, pp. 665-685, May/June, 1993. [8] N. Kasabov, “Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering,” The MIT Press, CA, MA, 550 pages, 1996. [9] N. Kasabov, “Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems,” Fuzzy Sets and Systems, vol. 82, pp. 135-149, 1996. [10] N. Kasabov, J. Kim, M. Watts, and A. Gray, “FuNN/2-A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition,” Information Sciences, Vol. 101, No. 3, pp. 155-175, 1997. [11] J. Kim, A. Mowat, P. Poole, and N. Kasabov, “Neuro-Fuzzy and Multivariate Statistical Classification of Fruit Populations based on Visible-Near Infrared Spectrophotometry Data,” In Proc. the Int. Conf. on Neural Info. Processing Data (ICONIP’97), Dunedin, New Zealand, Nov., Vol. 2, pp. 780-784, 1997. [12] T. Kohonen, “Self-Organisation and Associative Memory,” Springer-Verlag, New York, 1989. [13] R. Kozma, M. Sakuma, Y. Yokoyama, M. Kitamura, “On the Accuracy of Mapping by Neural Networks Trained by Backpropagation with Forgetting,” Neurocomputing, Vol. 13, pp. 295-311, 1996. [14] C.T. Lin and C.S.G. Lee, “Neural-Networks-Based Fuzzy Logic Control and Decision System,” IEEE Trans. on Computers, Vol. 40, No. 12, pp. 1320-1366, December, 1991. [15] J.R. Long, V.G. Gregoriou, and P.J. Gemperline, “Spectroscopic Calibration and Quantitation Using Artificial Neural Networks,” Anal. Chem, Vol. 62, No. 17, pp. 1792-1797, September 1, 1990. [16] A.D. Mowat and P.R. Poole, “Non-destructive discriminiation of persimmon fruit quality using visible-near infrared reflectance spectrophotometry,” Acta Horticulture, Vol. 436, pp. 159-163, 1997. [17] A.D. Mowat and P.R. Poole, “Use of visible-near infrared diffuse reflectance spectroscopy to discriminate kiwifruit whose properties were altered by preharvest treatments,” J. Near Infrared Spectroscopy, 1997, submitted. [18] A.D. Mowat and P.R. Poole, “Spectroscopic methods for characterising temporal changes in stored and ripening fruit,” Proceedings of the Australian Postharvest Conference, Hwakesbury, New South Wales, Australia, 1997, in press. [19] F. Murtagh and a. Heck, “Multivariate Data Analysis,” Kluwer Academic, Dordrecht, 1987. [20] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart and James L. McClelland, Eds., Vol. 1, Chap. 8, pp. 318-362, Cambridge, MA: MIT Press, 1986. [21] T. Sato, “Application of an artificial neural network to the identification of amino acids from near infrared spectral data,” J. Near Infrared Spectrosc., Vol. 1, pp. 199-208, 1993. [22] L. A. Zadeh, “Fuzzy Sets,” Information and Control, Vol. 8, pp. 338-353, 1965. [23] ───, “Outline of a new approach to the analysis of complex systems and decision process,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3, No. 1, pp. 28-44, January, 1973

Connectionist methods for classification of fruit populations based on visible-near infrared spectrophotometry data

Please note that this is a searchable PDF derived via optical character recognition (OCR) from the original source document. As the OCR process is never 100% perfect, there may be some discrepancies between the document image and the underlying text.Variation in fruit maturation can influence harvest timing and duration, post-harvest fruit attributes and consumer acceptability. Present methods of managing and identifying lines of fruit with specific attributes both in commercial fruit production systems and breeding programs are limited by a lack of suitable tools to characterise fruit attributes at different stages of development in order to predict fruit behaviour at harvest, during storage or in relation to consumer acceptance. With visible-near infrared (VNIR) reflectance spectroscopy a vast array of analytical information is collected rapidly with a minimum of sample pre-treatment. VNIR spectra contain information about the amount and the composition of constituents within fruit. This information can be obtained from intact fruit at different stage of development. Spectroscopic data is processed using chemometrics techniques such as principal component analysis (PCA), discriminant analysis and/or connectionist approaches in order to extract qualitative and quantitative information for classification and predictive purposes. In this paper, we will illustrate the effectiveness of a model, connectionist and hybrid approaches, for fruit quality classification problems.Unpublished[1] J.G. Cruz-Castillo, S. Ganeshanandam, B.R. MacKay, G.S. Lewes, C.R.O. Lawoko, and D.J. Woolley, “Applications of canonical discriminant analysis in horticultural research,” HortScience, pp. 1115-1119, 1994. [2] P. Geladi and E. Dåbakk, “An overview of chemometrics applications in near infrared spectrometry,” J. Near Infrared Spectrosc., Vol. 3, pp. 119-132, 1995. [3] M. Hana, W.F. McClure, and T.B. Whitaker, Applying artificial neural networks. I. Estimating nicotine in tobacco from near infrared data,” J. Near Infrared Spectrosc., Vol. 3, pp. 133-142, 1995. [4] W. Hauptmann, and K. Heesche, “A Neural Net Topology for Bidirectional Fuzzy-Neuro Transformation,” Proc. of the Fuzz-IEEE/IFES, Yokohama, Japan, pp. 1511-1518, 1995. [5] C. Hervás, A. Garrido, B. Lucena, N. García, and E. De Pedro, “Near infrared spectroscopy for classification of Iberian pig carcasses using an artificial neural network.” J. Near Infrared Spectrosc., Vol. 2, pp. 177-184, 1994. [6] M. Ishikawa, “Structural Learning with Forgetting,” Neural Networks, Vol. 9, No. 3, pp. 509-521, 1996. [7] J.-S. Roger Jang, “ANFIS: Adaptive-network-based fuzzy inference systems,” IEEE Trans. on System, Man and Cybernetics, Vol. 23, No. 3, pp. 665-685, May/June, 1993. [8] N. Kasabov, “Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering,” The MIT Press, CA, MA, 550 pages, 1996. [9] N. Kasabov, “Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems,” Fuzzy Sets and Systems, vol. 82, pp. 135-149, 1996. [10] N. Kasabov, J. Kim, M. Watts, and A. Gray, “FuNN/2-A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition,” Information Sciences, Vol. 101, No. 3, pp. 155-175, 1997. [11] J. Kim, A. Mowat, P. Poole, and N. Kasabov, “Neuro-Fuzzy and Multivariate Statistical Classification of Fruit Populations based on Visible-Near Infrared Spectrophotometry Data,” In Proc. the Int. Conf. on Neural Info. Processing Data (ICONIP’97), Dunedin, New Zealand, Nov., Vol. 2, pp. 780-784, 1997. [12] T. Kohonen, “Self-Organisation and Associative Memory,” Springer-Verlag, New York, 1989. [13] R. Kozma, M. Sakuma, Y. Yokoyama, M. Kitamura, “On the Accuracy of Mapping by Neural Networks Trained by Backpropagation with Forgetting,” Neurocomputing, Vol. 13, pp. 295-311, 1996. [14] C.T. Lin and C.S.G. Lee, “Neural-Networks-Based Fuzzy Logic Control and Decision System,” IEEE Trans. on Computers, Vol. 40, No. 12, pp. 1320-1366, December, 1991. [15] J.R. Long, V.G. Gregoriou, and P.J. Gemperline, “Spectroscopic Calibration and Quantitation Using Artificial Neural Networks,” Anal. Chem, Vol. 62, No. 17, pp. 1792-1797, September 1, 1990. [16] A.D. Mowat and P.R. Poole, “Non-destructive discriminiation of persimmon fruit quality using visible-near infrared reflectance spectrophotometry,” Acta Horticulture, Vol. 436, pp. 159-163, 1997. [17] A.D. Mowat and P.R. Poole, “Use of visible-near infrared diffuse reflectance spectroscopy to discriminate kiwifruit whose properties were altered by preharvest treatments,” J. Near Infrared Spectroscopy, 1997, submitted. [18] A.D. Mowat and P.R. Poole, “Spectroscopic methods for characterising temporal changes in stored and ripening fruit,” Proceedings of the Australian Postharvest Conference, Hwakesbury, New South Wales, Australia, 1997, in press. [19] F. Murtagh and a. Heck, “Multivariate Data Analysis,” Kluwer Academic, Dordrecht, 1987. [20] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart and James L. McClelland, Eds., Vol. 1, Chap. 8, pp. 318-362, Cambridge, MA: MIT Press, 1986. [21] T. Sato, “Application of an artificial neural network to the identification of amino acids from near infrared spectral data,” J. Near Infrared Spectrosc., Vol. 1, pp. 199-208, 1993. [22] L. A. Zadeh, “Fuzzy Sets,” Information and Control, Vol. 8, pp. 338-353, 1965. [23] ───, “Outline of a new approach to the analysis of complex systems and decision process,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3, No. 1, pp. 28-44, January, 1973