IEEE Transactions on Neural Networks, volume 11 (2000), number 1

• He, B. and H. Yang, A Neural-Network Model for Monotone Linear Asymmetric Variational Inequalities, pp. 3-16.
• Fukumizu, K., Statistical Active Learning in Multilayer Perceptrons, pp. 17-26.
• Holmes, C.C. and B.K. Mallick, Bayesian Wavelet Networks for Nonparametric Regression, pp. 27-35.
• Yamamoto, Y and P.N. Nikiforuk, A New Supervised Learning Algorithm for Multilayered and Interconnected Neural Networks, pp. 36-46.
• Wang, Z.-Q., M.T. Manry, and J.L. Schiano, LMS Learning Algorithms: Misconceptions and New Results on Convergence, pp. 47-56.
• Shimono, M. and T. Yamakawa, Design and Analysis of a Nonequilibrium Cross-Coupled Network with a Detectable Similarity Measure, pp. 57- 68.
• Seshagiri, S. and H.K. Khalil, Output Feedback Control of Nonlinear Systems Using RBF Neural Networks, pp. 69-79.
• Rivals, I. and L. Personnaz, Nonlinear Internal Model Control Using Neural Networks: Application to Processes with Delay and Design Issues, pp. 80-90.
• Trunov, A.B. and M.M. Polycarpou, Automated Fault Diagnosis in Nonlinear Multivariable Systems Using a Learning Methodology, pp. 91-101.
• Adetona, O., E. Garcia, and L.H. Keel, A New Method for the Control of Discrete Nonlinear Dynamic Systems Using Neural Networks, pp. 102- 112.
• Maghami, P.G. and D.W.Jr. Sparks, Design of Neural Networks for Fast Convergence and Accuracy: Dynamics and Control, pp. 113-123.
• Keerthi, S.S., S.K. Shevade, C. Bhattacharyya, and K.R.K. Murthy, A Fast Iterative Nearest Point Algorithm for Support Vector Machine Classifier Design, pp. 124-136.
• Doulamis, A.D., N.D. Doulamis, and S.D. Kollias, On-Line Retrainable Neural Networks: Improving the Performance of Neural Networks in Image Analysis Problems, pp. 137-155.
• Perry, S.W. and L. Guan, Weight Assignment for Adaptive Image Restoration by Neural Networks, pp. 156-170.
• Liang, S.-F., A.W.Y. Su, and C.-T. Lin, Model-Based Synthesis of Plucked String Instruments by Using a Class of Scattering Recurrent Networks, pp. 171-195.
• Khabou, M.A. and P.D. Gader, Automatic Target Detection Using Entropy Optimized Shared-Weight Neural Networks, pp. 186-193.
• Yang, J.-F. and C.-M. Chen, Winner-Take-All Neural Networks Using the Highest Threshold, pp. 194-199.
• Zhang, Q. and Y.-W. Leung, A Class of Learning Algorithms for Principal Component Analysis and Minor Component Analysis, pp. 200-204.
• Townley, S., A. Ilchmann, M.G. Weiss, W. McClements, A.C. Ruiz, D.H. Owens, and D. Pratzel-Wolters, Existence and Learning of Oscillations in Recurrent Neural Networks, pp. 205-214.
• Ouyang, S., Z. Bao, andG.-S. Liao, Robust Recursive Least Squares Learning Algorithm for Principal Component Analysis, pp. 215-221.
• Suykens, J.S.K., B. De Moor, and J. Vandewalle, Robust Local Stability of Multilayer Recurrent Neural Networks, pp. 222-229.
• Chen, Y.-H. and S.-C. Fang, Neurocomputing with Time Delay Analysis for Solving Convex Quadratic Programming Problems, pp. 230-240.
• Singh, R., V. Cherkassky, and N. Papanikolopoulos, Self-Organizing Maps for the Skeletonization of Sparse Shapes, pp. 241-248.
• Bellazzi, R., R. Guglielmann, and L. Ironi, How to Improve Fuzzy-Neural System Modeling by Means of Qualitative Simulation, pp. 249-252.
• Chakraborty, G., A Novel Normalization Technique for Unsupervised Learning in ANN, pp. 253-257.

IEEE Transactions on Neural Networks, volume 11 (2000), number 2

• Vila, J.-P., V. Wagner, and P. Neveu, Bayesian Nonlinear Model Selection and Neural Networks: A Conjugate Prior Approach, pp. 265-278.
• Clausen, C. and H. Wechsler, Quad-Q-Learning, pp. 279-294.
• Rubanov, N.S., The Layer-Wise Method and the Backpropagation Hybrid Approach to Learning a Feedforward Neural Network, pp. 295-305.
• Gomm, J.B. and D.L. Yu, Selecting Radial Basis Function Network Centers with Recursive Orthogonal Least Squares Training, pp. 306-314.
• Mandic, D.P. and J.A. Chambers, On the Choice of Parameters of the Cost Function in Nested Modular RNN's, pp. 315-322.
• Meir, R. and V.E. Maiorov, On the Optimality of Neural-Network Approximation Using Incremental Algorithms, pp. 323-337.
• Chatterjee, C., Z. Kang, and V.P. Roychowdhury, Algorithms for Accelerated Convergence of Adaptive PCA, pp. 338-355.
• Li, H.-X. and C.L.P. Chen, The Equivalence Between Fuzzy Logic Systems and Feedforward Neural Networks, pp. 356-365.
• Pal, S.K., R.K. De, and J. Basak, Unsupervised Feature Evaluation: A Neuro-Fuzzy Approach, pp. 366-376.
• Tsukimoto, H., Extracting Rules from Trained Neural Networks, pp. 377- 389.
• Choi, J.Y. and J.A. Farrel, Nonlinear Adaptive Control Using Networks of Piecewise Linear Approximators, pp. 390-401.
• Gorp, J. Van, J. Schoukens, and R. Pintelon, Learning Neural Networks with Noisy Inputs Using the Errors-in-Variables Approach, pp. 402-414.
• Fu, L.M., Discrete Probability Estimation for Classification Using Certainty-Factor-Based Neural Networks, pp. 415-422.
• Eaton, P.H., D.V. Prokhorov, and D.C. Wunsch, Neurocontroller Alternatives for "Fuzzy" Ball-and-Beam Systems with Nonuniform Nonlinear Friction, pp. 423-435.
• Parekh, R., J. Yang, and V. Honavar, Constructive Neural-Network Learning Algorithms for Pattern Classification, pp. 436-451.
• Lotlikar, R. and R. Kothari, Bayes-Optimality Motivated Linear and Multilayered Perceptron-Based Dimensionality Reduction, pp. 452-463.
• Khotanzad, A., H. Elragal, and T.-L. Lu, Combination of Artificial Neural- Network Forecasters for Prediction of Natural Gas Consumption, pp. 464-473.
• Yang, S. and D. Wang, Constraint Satisfaction Adaptive Neural Network and Heuristics Combined Approaches for Generalized Job-Shop Scheduling, pp. 474-486.
• Stan, O. and E. Kamen, A Local Linearized Least Squares Algorithm for Training Feedforward Neural Networks, pp. 487-495.
• Aladjem, M., Recursive Training of Neural Networks for Classification, pp. 496-503.
• Skurichina, M., S. Raudys, and R.P.W. Duin, K-Nearest Neighbors Directed Noise Injection in Multilayer Perceptron Training, pp. 504-511.
• Setiono, R., Extracting M-of-N Rules from Trained Neural Networks, pp. 512-519.
• Park, D.-C., Centroid Neural Network for Unsupervised Competitive Learning, pp. 520-528.
• Zhang, Q. and Y.-W. Leung, A Class of Learning Algorithms for Principal Component Analysis and Minor Component Analysis, pp. 529-533.
• Guan, Z.-H., G. Chen, and Y. Qin, On Equilibria, Stability, and Instability of Hopfield Neural Networks, p. 534].

IEEE Transactions on Neural Networks, volume 11 (2000), number 3

• Bengio, Y., J.M. Buhmann, M. Embrechts, and J.M. Zurada, Introduction to the Special Issue on Neural Networks for Data Mining and Knowledge Discovery, pp. 545-549.
• Bengio, S. and Y. Bengio, Taking on the Curse of Dimensionality in Joint Distributions Using Neural Networks, pp. 550-557.
• Yan, L. and D.J. Miller, General Statistical Inference for Discrete and Mixed Spaces By an Approximate Application of the Maximum Entropy Principle, pp. 558-573.
• Kohonen, T., S. Kaski, K.K. Lagus, J. Salojarvi, V. Paatero, and A. Saarela, Organization of a Massive Document Collection, pp. 574-585.
• Vesanto, J. and E. Alhoniemi, Clustering of the Self-Organizing Map, pp. 586-600.
• Alahakoon, D., S.K. Halgamuge, and B. Srinivasan, Dynamic Self-Organizing Maps with Controlled Growth for Knowledge Discovery, pp. 601-614.
• Konig, A., Interactive Visualization and Analysis of Hierarchical Neural Projections for Data Mining, pp. 615-624.
• Wang, Y., L. Luo, M.T. Freedman, and S.-Y. Kung, Probabilistic Principal Component Subspaces: A Hierarchical Finite Mixture Model for Data Visualization, pp. 625-636.
• Shin, C.K., S.J. Yu, U.T. Yun, and H K. Kim, A Hybrid Approach of Neural Network and Memory-Based Learning to Data Mining, pp. 637-646.
• Fu, L.M. and E.H. Shortliffe, The Application of Certainty Factors to Neural Computing for Rule Discovery, pp. 647-657.
• Zhang, Y.-Q., M.D. Fraser, R.A. Gagliano, and A. Kandel, Granular Neural Networks for Numerical-Linguistic Data Fusion and Knowledge Discovery, pp. 658-667.
• Kewley, R., M. Embrechts, and C. Breneman, Data Strip Mining for the Virtual Design of Pharmaceuticals with Neural Networks, pp. 668-679.
• Lee, R.S.T. and J.N.K. Liu, Tropical Cyclone Identification and Tracking System Using Integrated Neural Oscillatory Elastic Graph Matching and Hybrid RBF Network Track Mining Techniques, pp. 680-689.
• Mozer, M.C., R. Wolniewicz, D.B. Grimes, E. Johnson, and H. Kaushansky, Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunications Industry, pp. 690-696.
• Atiya, A.F. and A.G. Parlos, New Results on Recurrent Network Training: Unifying the Algorithms and Accelerating Convergence, pp. 697-709.
• Castro, J.L., M. Delgado, and C.J. Mantas, SEPARATE: A Machine Learning Method Based on Semi-Global Partitions, pp. 710-720.
• Su, M.-C. and H.-T. Chang, Fast Self-Organizing Feature Map Algorithm, pp. 721-733.
• Hoppensteadt, F.C. and E.M. Izhikevich, Pattern Recognition Via Synchronization in Phase-Locked Loop Neural Networks, pp. 734-738.
• Zhang, C.N., M. Zhao, and M. Wang, Logic Operations Based on Single Neuron Rational Model, pp. 739-747.
• Mitra, S. and Y. Hayashi, Neuro-Fuzzy Rule Generation: Survey in Soft Computing Framework, pp. 748-768.
• Gabrys, B. and A. Bargiela, General Fuzzy Min-Max Neural Network for Clustering and Classification, pp. 769-783.
• Azimi-Sadjadi, M.R., D. Yao, Q. Huang, and G. J. Dobeck, Underwater Target Classification Using Wavelet Packets and Neural Networks, pp. 784- 794.
• Lehtokangas, M., Cascade-Correlation Learning for Classification, pp. 795-798.
• Huang, G.-B., Y.-Q. Chen, and H.A. Babri, Classification Ability of Single Hidden Layer Feedforward Neural Networks, pp. 799-801.
• Maire, F., On the Convergence of Validity Interval Analysis, pp. 802- 807.
• Yang, T.-N. and S.-D. Wang, Fuzzy Auto-Associative Neural Networks for Principal Component Extraction of Noisy Data, pp. 808-810.
• Cabrelli, C., U. Molter, and R. Shonkwiler, A Constructive Algorithm to Solve "Convex Recursive Deletion" (CoRD) Classification Problems via Two-Layer Perceptron Networks, pp. 811-816.
• Aonishi, T. and K. Kurata, Extension of Dynamic Link Matching by Introducing Local Linear Maps, pp. 817-822.

IEEE Transactions on Neural Networks, volume 11 (2000), number 4

• Calvert, B.D. and C.A. Marinov, Another K-Winners-Take-All Analog Neural Network, pp. 829-838.
• Ojha, P.C., Enumeration of Linear Threshold Functions from the Lattice of Hyperplane Intersections, pp. 839-850.
• Uykan, Z., C. Guzelis, M.E. Celebi, and H.N. Koivo, Analysis of Input- Output Clustering for Determining Centers of RBFN, pp. 851-858.
• Wu, Y. and D.A. Pados, A Feedforward Bidirectional Associative Memory, pp. 859-866.
• Giminez-Martinez, V., A Modified Hopfield Auto-Associative Memory with Improved Capacity, pp. 867-878.
• Bharitkar, S. and J.M. Mendel, The Hysteretic Hopfield Neural Network, pp. 879-888.
• Hong, X. and C.J. Harris, Generalized Neurofuzzy Network Modeling Algorithms Using Bezier-Bernstein Polynomial Functions and Additive Decomposition, pp. 889-902.
• Tian, B., M.R. Azimi-Sadjadi, T.H. Vonder Haar, and D. Reinke, Temporal Updating Scheme for Probabilistic Neural Network with Application to Satellite Cloud Classification, pp. 903-920.
• Sabisch, T., A. Ferguson, and H. Bolouri, Identification of Complex Shapes Using a Self Organizing Neural System, pp. 921-934.
• Cesmeli, E. and D. Wang, Motion Segmentation Based on Motion/Brightness Integration and Oscillatory Correlation, pp. 935-947.
• Gutta, S., J. Huang, P.J. Phillips, and H. Wechsler, Mixture of Experts for Classification of Gender, Ethnic Origin, and Pose of Human Faces, pp. 948-960.
• Mak, M.-W. and S.-Y. Kung, Estimation of Elliptical Basis Function Parameters by the EM Algorithm with Application to Speaker Verification, pp. 961-969.
• Hakkinen, J., M. Lagerholm, C. Peterson, and B. Soderberg, Local Routing Algorithms Based on Potts Neural Networks, pp. 970-977.
• Jones, S., R. Meddis, S.C. Lim, and A.R. Temple, Toward a Digital Neuromorphic Pitch Extraction System, pp. 978-987.
• Mos, E.C., J.J.L. Hoppenbrouwers, M.T. Hill, M.W. Blum, J.J.H.B. Schleipen, and H. de Waardt, Optical Neuron by Use of a Laser Diode with Injection Seeding and External Optical Feedback, pp. 988-996.
• Arslan, G. and F.A. Sakarya, A Unified Neural-Network-Based Speaker Localization Technique, pp. 997-1002.
• Chang, C.-C., C.-W. Hsu, and C.-J. Lin, The Analysis of Decomposition Methods for Support Vector Machines, pp. 1003-1008.
• Mao, K.Z., K.-C. Tan, and W. Ser, Probabilistic Neural-Network Structure Determination for Pattern Classification, pp. 1009-1016.
• Xia, Y. and J. Wang, Global Exponential Stability of Recurrent Neural Networks for Solving Optimization and Related Problems, pp. 1017- 1022.
• Kumar, S., Memory Annihilation of Structured Maps in Bidirectional Associative Memories, pp. 1023-1030.
• Meng, Z. and Y.-H. Pao, Visualization and Self-Organization of Multidimensional Data through Equalized Orthogonal Mapping, pp. 1031-1038.

IEEE Transactions on Neural Networks, volume 11 (2000), number 5

• Lagaris, I.E., A.C. Likas, and D.G. Papageorgiou, Neural-Network Methods for Boundary Value Problems with Irregular Boundaries, pp. 1041-1049.
• Karystinos, G.N. and D.A. Pados, On Overfitting, Generalization, and Randomly Expanded Training Sets, pp. 1050-1057.
• Wu, Y. and S.N. Batalama, An Efficient Learning Algorithm for Associative Memories, pp. 1058-1066.
• Chuang, C.-C., S.-F. Su, and C.-C. Hsiao, The Annealing Robust Backpropagation (ARBP) Learning Algorithm., pp. 1067-1077.
• Lin, C.-L., C.-C. Lai, and T.-H. Huang, A Neural Network for Linear Matrix Inequality Problems, pp. 1078-1092.
• Karayiannis, N.B., Soft Learning Vector Quantization and Clustering Algorithms Based on Ordered Weighted Aggregation Operators, pp. 1093-1105.
• Chen, K., D. Wang, and X. Liu, Weight Adaptation and Oscillatory Correlation for Image Segmentation, pp. 1106-1123.
• Basak, J. and D. Mahata, A Connectionist Model for Corner Detection in Binary and Gray Images, pp. 1124-1132.
• Leung, H., G. Hennessey, and A. Drosopoulos, Signal Detection Using the Radial Basis Function Coupled Map Lattice, pp. 1133-1151.
• Ko, H. and G.M. Jacyna, Dynamical Behavior of Autoassociative Memory Performing Novelty Filtering for Signal Enhancement, pp. 1152-1161.
• Kwok, J.T.-Y., The Evidence Framework Applied to Support Vector Machines, p. 11621173.
• Zhang, D. and S.K. Pal, A Fuzzy Clustering Neural Networks (FCNs) System Design Methodology, pp. 1174-1177.
• Kwan, C.M. and F.L. Lewis, Robust Backstepping Control of Induction Motors Using Neural Networks, pp. 1178-1187.
• Shevade, S.K., S.S. Keerthi, C. Bhattacharyya, and K.R.K. Murthy, Improvements to the SMO Algorithm for SVM Regression, pp. 1188- 1193.
• Liang, X.-B., Equivalence Between Local Exponential Stability of the Unique Equilibrium Point and Global Stability for Hopfield-Type, Neural Networks with Two Neurons, pp. 1194-1196.

IEEE Transactions on Neural Networks, volume 11 (2000), number 6

• Grandvalet, Y., Anisotropic Noise Injection for Input Variables Relevance Determination, pp. 1201-1212.
• Ciesielski, K., J.P. Sacha, and K.J. Cios, Synthesis of Feedforward Networks in Supremum Error Bound, pp. 1213-1227.
• Bomze, I.M., M. Pelillo, and V. Stix, Approximating the Maximum Weight Clique Using Replicator Dynamics, pp. 1228-1241.
• Weingessel, A. and K. Hornik, Local PCA Algorithms, pp. 1242-1250.
• Liang, X.-B. and J. Wang, A Recurrent Neural Network for Nonlinear Optimization with a Continuously Differentiable Objective Function and Bound Constraints, pp. 1251-1262.
• Saerens, M., Building Cost Functions Minimizing to Some Summary Statistics, pp. 1263-1271.
• Jiang, D. and J. Wang, On-Line Learning of Dynamical Systems in the Presence of Model Mismatch and Disturbances, pp. 1272-1283.
• Grippo, L., Convergent On-Line Algorithms for Supervised Learning in Neural Networks, pp. 1284-1299.
• Chen, S. and J. Weng, State-Based SHOSLIF for Indoor Visual Navigation, pp. 1300-1314.
• Benson, M.W. and J. Hu, Asynchronous Self-Organizing Maps, pp. 1315- 1322.
• Kakeya, H. and Y. Okabe, Fast Combinatorial Optimization with Parallel Digital Computers, pp. 1323-1331.
• Azeem, M.F., M. Hanmandlu, and N. Ahmad, Generalization of Adaptive Neuro- Fuzzy Inference Systems, pp. 1332-1346.
• Zhang, Y., P.-Y. Peng, and Z.-P. Jiang, Stable Neural Controller Design for Unknown Nonlinear Systems Using Backstepping, pp. 1347-1360.
• Krishna, K., M.A.L. Thathachar, and K.R. Ramakrishnan, Voronoi Networks and Their Probability of Misclassification, pp. 1361-1372.
• Fu, H.-C., H.Y. Chang, Y.Y. Xu, and H.T. Pao, User Adaptive Handwriting Recognition by Self-Growing Probabilistic Decision-Based Neural Networks, pp. 1373-1384.
• Szatmari, I., A. Schultz, C. Rekeczky, T. Kozek, T. Roska, and L.O. Chua, Morphology and Autowave Metric on CNN Applied to Bubble-Debris Classification, pp. 1385-1393.
• Tsujitani, M. and T. Koshimizu, Neural Discriminant Analysis, pp. 1394-1401.
• Medeiros, M.C. and A. Veiga, %T A Hybrid Linear-Neural Model for Time Series Forecasting, pp. 1402-1412.
• Li, Y., J. Wang, and J.M. Zurada, Blind Extraction of Singularly Mixed Source Signals, pp. 1413-1422.
• Cruces-Alvarez, S., A. Cichocki, and L. Castedo-Ribas, An Iterative Inversion Approach to Blind Source Separation, pp. 1423-1437.
• Vassiliadis, S., M. Zhang, and J.G. Delgado-Frias, Elementary Function Generators for Neural-Network Emulators, pp. 1438-1449.
• Cruz-Cabrera, A.A., M. Yang, E.C. Behrman, J.E. Steck, and S.R. Skinner, Reinforcement and Backpropagation Training for an Optical Neural Network Using Self-Lensing Effects, pp. 1450-1457.
• Gibbs, M.N. and D.J.C. MacKay, Variational Gaussian Process Classifiers, pp. 1458-1464.
• Lee, M. and H.-S. Choi, A Robust Neural Controller for Underwater Robot Manipulators, pp. 1465-1470.
• Da, F., Decentralized Sliding Mode Adaptive Controller Design Based on Fuzzy Neural Networks for Interconnected Uncertain Nonlinear Systems, pp. 1471-1480.
• Liao, T.-L. and F.-C. Wang, Global Stability for Cellular Neural Networks with Time Delay, pp. 1481-1484.
• Luo, Y. and S. Shen, L^p Approximation of Sigma-Pi Neural Networks, pp. 1485-1489.
• Douglas, S.C., Self-Stabilized Gradient Algorithms for Blind Source Separation with Orthogonality Constraints, pp. 1490-1497.
• Cho, S.-Y. and T.W.S. Chow, Learning Parametric Specular Reflectance Model by Radial Basis Function Network, pp. 1498-1502.
• Wang, L., HeteroAssociations of Spatio-Temporal Sequences with the Bidirectional Associative Memory, pp. 1503-1505.
• Liang, X.-B., A Comment on "On Equilibria, Stability, and Instability of Hopfield Neural Networks", p. 1506.
• Guan, Z.-H., G. Chen, and Y. Qin, Author's Reply, p. 1507.