IEEE Transactions on Neural Networks, volume 10 (1999), number 1

• Zurada, J.M., Challenges and Changes, pp. 1-2.
• Gelenbe, E., Z.-H. Mao, and Y.-D. Li, Function Approximation with Spiked Random Networks, pp. 3-9.
• Shah, J.V. and C.-S. Poon, Linear Independence of Internal Representations in Multilayer Perceptrons, pp. 10-18.
• Igelnik, B., Y.-H. Pao, S.R. LeClair, and C.Y. Shen, The Ensemble Approach to Neural-Network Learning and Generalization, pp. 19-30.
• Ridella, S., S. Rovetta, and R. Zunino, Representation and Generalization Properties of Class-Entropy Networks, pp. 31-47.
• RoyChowdhury, P., Y.P. Singh, and R.A. Chansarkar, Dynamic Tunneling Technique for Efficient Training of Multilayer Perceptrons, pp. 48- 55.
• Choueiki, M.H. and C.A. Mount-Campbell, Training Data Development with the D-Optimality Criterion, pp. 56-63.
• Sum, J.P.F., C.S. Leung, P.K.S. Tam, G.H. Young, W.K. Kan, and L.-w. Chan, Analysis for a Class of Winner-Take-All Model, pp. 64-71.
• Gall, A. Le and V. Zissimopoulos, Extended Hopfield Models for Combinatorial Optimization, pp. 72-80.
• Jiang, D. and J. Wang, A Recurrent Neural Network for Real-Time Semidefinite Programming, pp. 81-93.
• Chen, C.-H. and V. Honavar, A Neural-Network Architecture for Syntax Analysis, pp. 94-114.
• Panagiotopoulos, D.A., R.W. Newcomb, and S.K. Singh, Planning with a Functional Neural-Network Architecture, pp. 115-127.
• Jagannathan, S., Discrete-Time CMAC NN Control of Feedback Linearizable Nonlinear Systems Under a Persistence of Excitation, pp. 128-137.
• Tian, B., M.A. Shaikh, M.R. Azimi-Sadjadi, T.H. Vonder Haar, and D. Reinke, A Study of Cloud Classification with Neural Networks Using Spectral and Textural Features, pp. 138-151.
• Ramamurti, V. and J. Ghosh, Structurally Adaptive Modular Networks for Nonstationary Environments, pp. 152-160.
• Sum, J., C.-s. Leung, G.H. Young, and W.-k. Kan, On the Kalman Filtering Method in Neural-Network Training and Pruning, pp. 161-166.
• Sivakumar, S.C., W. Robertson, and W.J. Phillips, On-Line Stabilization of Block-Diagonal Recurrent Neural Networks, pp. 167-175.
• Sandalidis, H.G., P.P. Stavroulakis, and J. Rodriguez-Tellez, Borrowing Channel Assignment Strategies Based on Heuristic Techniques for Cellular Systems, pp. 176-181.
• Kakeya, H. and Y. Okabe, Selective Retrieval of Memory and Concept Sequences Through Neuro-Windows, pp. 182-185.
• Filippidis, A., L.C. Jain, and P. Lozo, Degree of Familiarity ART2 in Knowledge-Based Landmine Detection, pp. 186-192.
• Suganthan, P.N., Hierarchical Overlapped SOM's for Pattern Classification, pp. 193-195.
• Altug, S., H.J. Trussell, and M.-Y. Chow, A "Mutual Update" Training Algorithm for Fuzzy Adaptive Logic Control/Decision Network (FALCON), pp. 196-198.
• Tagliaferri, R., N. Capuano, and G. Gargiulo, Automated Labeling for Unsupervised Neural Networks: A Hierarchical Approach, pp. 199-203.
• Hulle, M.M. Van, Density-Based Clustering with Topographic Maps, pp. 204-206.
• Taleb, A. and G. Cirrincione, Against the Convergence of the Minor Component Analysis Neurons, pp. 207-210.

IEEE Transactions on Neural Networks, volume 10 (1999), number 2

• Townsend, N.W. and L. Tarassenko, Estimations of Error Bounds for Neural- Network Function Approximators, pp. 217-230.
• Sarajedini, A., R. Hecht-Nielsen, and P.M. Chau, Conditional Probability Density Function Estimation with Sigmoidal Neural Networks, pp. 231- 238.
• Ku, K.W.C., M.W. Mak, and W.C. Siu, Adding Learning to Cellular Genetic Algorithms for Training Recurrent Neural Networks, pp. 239-252.
• Campolucci, P., A. Uncini, F. Piazza, and B.D. Rao, On-Line Learning Algorithms for Locally Recurrent Neural Networks, pp. 253-271.
• Younger, A.S., P.R. Conwell, and N.E. Cotter, Fixed-Weight On-Line Learning, pp. 272-283.
• Tinˇo, P. and M. Kšteles, Extracting Finite-State Representations from Recurrent Neural Networks Trained on Chaotic Symbolic Sequences, pp. 284-302.
• Tsypkin, Y.Z., J.D. Mason, E.D. Avedyan, K. Warwick, and I.K. Levin, Neural Networks for Identification of Nonlinear Systems Under Random Piecewise Polynomial Disturbances, pp. 303-312.
• Zhang, J. and A.J. Morris, Recurrent Neuro-Fuzzy Networks for Nonlinear Process Modeling, pp. 313-326.
• Iatrou, M., T.W. Berger, and V.Z. Marmarelis, Modeling of Nonlinear Nonstationary Dynamic Systems with a Novel Class of Artificial Neural Networks, pp. 327-339.
• Brdys, M.A. and G.J. Kulawski, Dynamic Neural Controllers for Induction Motor, pp. 340-355.
• Srinivasa, N. and N. Ahuja, A Topological and Temporal Correlator Network for Spatiotemporal Pattern Learning, Recognition, and Recall, pp. 356-371.
• Candocia, F.M. and J.C. Principe, Super-Resolution of Images Based on Local Correlations, pp. 372-380.
• Sigitani, T., Y. Iiguni, and H. Maeda, Image Interpolation for Progressive Transmission by Using Radial Basis Function Networks, pp. 381-390.
• Bouchard, M., B. Paillard, and C. Tan Le Dinh, Improved Training of Neural Networks for the Nonlinear Active Control of Sound and Vibration, pp. 391-401.
• Atiya, A.F., S.M. El-Shoura, S.I. Shaheen, and M.S. El-Sherif, A Comparison Between Neural-Network Forecasting Techniques--Case Study: River Flow Forecasting, pp. 402-409.
• Lehtokangas, M., Fast Initialization for Cascade-Correlation Learning, pp. 410-414.
• Anguita, D., S. Ridella, and S. Rovetta, Worst Case Analysis of Weight Inaccuracy Effects in Multilayer Perceptrons, pp. 415-418.
• Kantardzic, M.M., A.A. Aly, and A.S. Elmaghraby, Visualization of Neural- Network Gaps Based on Error Analysis, pp. 419-426.
• Iyer, M.S. and R.R. Rhinehart, A Method to Determine the Required Number of Neural Network Training Repetitions, pp. 427-432.
• Kremer, S.C., Identification of a Specific Limitation on Local-Feedback Recurrent Networks Acting as Mealy-Moore Machines, pp. 433-438.
• Li, S.Z. and J. Lu, Face Recognition Using the Nearest Feature Line Method, pp. 439-443.
• Suganthan, P.N., E.K. Teoh, and D.P. Mital, Hopfield Network with Constraint Parameter Adaptation for Overlapped Shape Recognition, pp. 444-449.
• Lee, J., C. Beach, and N. Tepedelenlioglu, A Practical Radial Basis Function Equalizer, pp. 450-455.
• Flynn, M.P. Walsh, M.E. and M.J. O'Malley, Augmented Hopfield Network for Mixed-Integer Programming, pp. 456-458.

IEEE Transactions on Neural Networks, volume 10 (1999), number 3

• Johnson, J.L., M.L. Padgett, and O. Omidvar, Overview of Pulse Coupled Neural Networks (PCNN) Special Issue, pp. 461-463.
• Eckhorn, R., Neural Mechanisms of Scene Segmentation: Recordings from the Visual Cortex Suggest Basic Circuits for Linking Field Models (Invited Paper), pp. 464-479.
• Johnson, J.L. and M.L. Padgett, PCNN Models and Applications, pp. 480- 498.
• Izhikevich, E.M., Class 1 Neural Excitability, Conventional Synapses, Weakly Connected Networks, and Mathematical Foundations of Pulse-Coupled Models, pp. 499-507.
• Izhikevich, E.M., Weakly Pulse-Coupled Oscillators, FM Interactions, Synchronization, and Oscillatory Associative Memory, pp. 508-526.
• Frank, G., G. Hartmann, A. Jahnke, and M. SchŠfer, An Accelerator for Neural Networks with Pulse-Coded Model Neurons, pp. 527-538.
• Ota, Y. and B.M. Wilamowski, Analog Implementation of Pulse-Coupled Neural Networks, pp. 539-544.
• Hikawa, H., Frequency-Based Multilayer Neural Network with On-Chip Learning and Enhanced Neuron Characterisitcs, pp. 545-553.
• Broussard, R.P., S.K. Rogers, M.E. Oxley, and G.L. Tarr, Physiologically Motivated Image Fusion for Object Detection using a Pulse Coupled Neural Network, pp. 554-563.
• Liu, X. and D.L. Wang, Range Image Segmentation Using a Relaxation Oscillator Network, pp. 564-573.
• Wilcox, M.J. and D.C.Jr. Thelen, A Retina with Parallel Input and Pulsed Output, Extracting High-Resolution Information, pp. 574-583.
• Kinser, J.M. and T. Lindblad, Implementation of Pulse-Coupled Neural Networks in a CNAPS Environment, pp. 584-590.
• Kuntimad, G. and H.S. Ranganath, Perfect Image Segmentation using Pulse Coupled Neural Networks, pp. 591-598.
• Clark, N., M. Banish, and H.S. Ranganath, Smart Adaptive Optic Systems Using Spatial Light Modulators, pp. 599-603.
• Caulfield, H.J. and J.M. Kinser, Finding the Shortest Path in the Shortest Time Using PCNN's, pp. 604-606.
• Lindblad, T. and J.M. Kinser, Inherent Features of Wavelets and Pulse Coupled Networks, pp. 607-614.
• Ranganath, H.S. and G. Kuntimad, Object Detection Using Pulse Coupled Neural Networks, pp. 615-620.
• Kinser, J.M., Foveation by a Pulse-Coupled Neural Network, pp. 621-625.
• HyvŠrinen, A., Fast and Robust Fixed-Point Algorithms for Independent Component Analysis, pp. 626-634.
• Palmieri, F., C. Catello, and G. D'Orio, Inhibitory Synapses in Neural Networks with Sigmoidal Nonlinearities, pp. 635-644.
• Cid-Sueiro, J., J.I. Arribas, S. Urban-Mun˜oz, and A.R. Figueiras-Vidal, Cost Functions to Estimate A Posteriori Probabilities in Multiclass Problems, pp. 645-656.
• Karayiannis, N.B., Reformulated Radial Basis Neural Networks Trained by Gradient Descent, pp. 657-671.
• Guarnieri, S., F. Piazza, and A. Uncini, Multilayer Feedforward Networks with Adaptive Spline Activation Function, pp. 672-683.
• Wang, D.L. and G.J. Brown, Separation of Speech from Interfering Sounds Based on Oscillatory Correlation, pp. 684-697.
• Bharitkar, S., K. Tsuchiya, and Y. Takefuji, Microcode Optimization with Neural Networks, pp. 698-703.
• Kawamura, M., M. Okada, and Y. Hirai, Dynamics of Selective Recall in an Associative Memory Model with One-to-Many Associations, pp. 704-713.
• McLain, R.B., M.A. Henson, and M. Pottmann, Direct Adaptive Control of Partially Known Nonlinear Systems, pp. 714-721.
• Tian, B., M.A. Shaikh, M.R. Azimi-Sadjadi, T.H. Vonder Haar, and D.L. Reinke, Errata to "A Study of Cloud Classification with Neural Networks Using Spectral and Textural Features", p. 722.

IEEE Transactions on Neural Networks, volume 10 (1999), number 4

• Citterio, C., A. Pelagotti, V. Piuri, and L. Rocca, Function Approximation--A Fast-Convergence Neural Approach Based on Spectral Analysis, pp. 725-740.
• Galicki, M., L. Leistritz, and H. Witte, Learning Continuous Trajectories in Recurrent Neural Networks with Time-Dependent Weights, pp. 741- 756.
• Lavoie, P., J.-F. Crespo, and Y. Savaria, Generalization, Discrimination, and Multiple Categorization Using Adaptive Resonance Theory, pp. 757-767.
• Dagher, I., M. Georgiopoulos, G.L. Heileman, and G. Bebis, An Ordering Algorithm for Pattern Presentation in Fuzzy ARTMAP That Tends to Improve GeneralizationPerformance, pp. 768-778.
• Duro, R.J. and J. Santos Reyes, Discrete-Time Backpropagation for Training Synaptic Delay-Based Artificial Neural Networks, pp. 779-789.
• Lysenko, M.G., H.-I. Wong, and G.I. Maldonado, Predicting Neutron Diffusion Eigenvalues with a Query-Based Adaptive Neural Architecture, pp. 790-800.
• Reyneri, L.M., Unification of Neural and Wavelet Networks and Fuzzy Systems, pp. 801-814.
• Figueiredo, M. and F. Gomide, Design of Fuzzy Systems Using Neurofuzzy Networks, pp. 815-827.
• Juang, C.-F. and C.-T. Lin, A Recurrent Self-Organizing Neural Fuzzy Inference Network, pp. 828-845.
• Lin, C.-T. and C.-P. Jou, Controlling Chaos by GA-Based Reinforcement Learning Neural Network, pp. 846-859.
• Sun, H., L. Liu, and A. Guo, A Neurocomputational Model of Figure-Ground Discrimination and Target Tracking, pp. 860-884.
• Kalkkuhl, J., K.J. Hunt, and H. Fritz, FEM-Based Neural-Network Approach to Nonlinear Modeling with Application to Longitudinal Vehicle Dynamics Control, pp. 885-897.
• Mayosky, M.A. and G.I.E. Cancelo, Direct Adaptive Control of Wind Energy Conversion Systems Using Gaussian Networks, pp. 898-906.
• Suykens, J.A.K. and J. Vandewalle, Training Multilayer Perceptron Classifiers Based on a Modified Support Vector Method, pp. 907-911.
• Luo, J., B. Hu, X.-T. Ling, and R.-W. Liu, Principal Independent Component Analysis, pp. 912-917.
• Fang, Y. and T.W.S. Chow, Blind Equalization of a Noisy Channel by Linear Neural Network, pp. 918-924.
• Zhang, L. and B. Zhang, A Geometrical Representation of McCulloch-Pitts Neural Model and Its Applications, pp. 925-929.
• Zhang, Y. and X.R. Li, A Fast U-D Factorization-Based Learning Algorithm with Applications to Nonlinear System Modeling andIdentification, pp. 930-938.
• Zhang, B., M. Fu, H. Yan, and M.A. Jabri, Handwritten Digit Recognition by Adaptive-Subspace Self-Organizing Map (ASSOM), pp. 939-945.
• Park, J., H. Cho, and D. Park, Design of GBSB Neural Associative Memories Using Semidefinite Programming, pp. 946-950.
• Morns, I.P. and S.S. Dlay, Analog Design of a New Neural Network for Optical Character Recognition, pp. 951-952.
• Sala, D.M. and K.J. Cios, Solving Graph Algorithms with Networks of Spiking Neurons, pp. 953-957.
• Gan, Q., P. Saratchandran, N. Sundararajan, and K.R. Subramanian, A Complex Valued Radial Basis Function Network for Equalization of Fast Time Varying Channels, pp. 958-959.
• Oh, S.-H. and S.-Y. Lee, A New Error Function at Hidden Layers for Fast Training of Multilayer Perceptrons, pp. 960-963.
• Ponnapalli, P.V.S., K.C. Ho, and M. Thomson, A Formal Selection and Pruning Algorithm for Feedforward Artificial Neural Network Optimization, pp. 964-967.
• Sprinkhuizen-Kuyper, I.G. and E.J.W. Boers, A Local Minimum for the 2-3-1 XOR Network, pp. 968-971.
• Ridella, S., S. Rovetta, and R. Zunino, Circular Backpropagation Networks Embed Vector Quantization, pp. 972-974.
• Lee, D.-L., New Stability Conditions for Hopfield Networks in Partial Simultaneous Update Mode, pp. 975-977.
• Kwok, T. and K.A. Smith, A Unified Framework for Chaotic Neural-Network Approaches to Combinatorial Optimization, pp. 978-981.

IEEE Transactions on Neural Networks, volume 10 (1999), number 5

• Cherkassky, V. and F. Mulier, Vapnik-Chervonenkis (VC) Learning Theory and Its Applications, pp. 985-987.
• Vapnik, V.N., An Overview of Statistical Learning Theory, pp. 988-999.
• Schšlkopf, B., S. Mika, C.J.C. Burges, P. Knirsch, K.-R. MŸller, G. RŠtsch, and A.J. Smola, Input Space Versus Feature Space in Kernel-Based Methods, pp. 1000-1017.
• Kwok, J.T.-Y., Moderating the Outputs of Support Vector Machine Classifiers, pp. 1018-1031.
• Mangasarian, O.L. and D.R. Musicant, Successive Overrelaxation for Support Vector Machines, pp. 1032-1037.
• Mattera, D., F. Palmieri, and S. Haykin, Simple and Robust Methods for Support Vector Machine Expansions, pp. 1038-1047.
• Drucker, H., D. Wu, and V.N. Vapnik, Support Vector Machines for Spam Categorization, pp. 1048-1054.
• Chapelle, O., P. Haffner, and V.N. Vapnik, Support Vector Machines for Histogram-Based Image Classification, pp. 1055-1064.
• Ben-Yacoub, S., Y. Abdeljaoued, and E. Mayoraz, Fusion of Face and Speech Data for Person Identity Verification, pp. 1065-1074.
• Cherkassky, V., X. Shao, F.M. Mulier, and V.N. Vapnik, Model Complexity Control for Regression Using VC Generalization Bounds, pp. 1075-1089.
• Thathachar, M.A.L. and M.T. Arvind, Global Boltzmann Perceptron Network for On-Line Learning of Conditional Distributions, pp. 1090-1098.
• Apolloni, B. and I. Zoppis, Subsymbolically Managing Pieces of Symbolical Functions for Sorting, pp. 1099-1122.
• Wang, J., Q. Hu, and D. Jiang, A Lagrangian Network for Kinematic Control of Redundant Robot Manipulators, pp. 1123-1132.
• Song, Q., J. Xiao, and Y.C. Soh, Robust Backpropagation Training Algorithm for Multilayered Neural Tracking Controller, pp. 1133-1141.
• Kuncheva, L.I. and J.C. Bezdek, Presupervised and Postsupervised Prototype Classifier Design, pp. 1142-1152.
• Karayiannis, N.B., An Axiomatic Approach to Soft Learning Vector Quantization and Clustering, pp. 1153-1165.
• Benbenisti, Y., D. Kornreich, H.B. Mitchell, and P.A. Schaefer, Fixed Bit- Rate Image Compression Using a Parallel-Structure Multilayer Neural Network, pp. 1166-1172.
• Basak, J. and S. Amari, Blind Separation of Uniformly Distributed Signals: A General Approach, pp. 1173-1185.
• Diamantaras, K.I., K. Hornik, and M.G. Strintzis, Optimal Linear Compression Under Unreliable Representation and Robust PCA Neural Models, pp. 1186-1195.
• Ding, A.A., Neural-Network Prediction with Noisy Predictors, pp. 1196- 1203.
• Tresp, V., T. Briegel, and J. Moody, Neural-Network Models for the Blood Glucose Metabolism of a Diabetic, pp. 1204-1213.
• Aggarwal, R.K., Q.Y. Xuan, A.T. Johns, F. Li, and A. Bennett, A Novel Approach to Fault Diagnosis in Multicircuit Transmission Lines Using Fuzzy ARTmap Neural Networks, pp. 1214-1221.
• Asai, T., M. Ohtani, and H. Yonezu, Analog Integrated Circuits for the Lotka-Volterra Competitive Neural Networks, pp. 1222-1231.
• Shin, J. and C. Koch, Dynamic Range and Sensitivity Adaptation in a Silicon Spiking Neuron, pp. 1232-1238.
• Chen, S., Y. Wu, and B.L. Luk, Combined Genetic Algorithm Optimization and Regularized Orthogonal Least Squares Learning for Radial Basis Function Networks, pp. 1239-1243.
• Lu, B.-L. and M. Ito, Task Decomposition and Module Combination Based on Class Relations: A Modular Neural Network for Pattern Classification, pp. 1244-1256.
• Wang, B., J. Nie, and Z. He, A Transiently Chaotic Neural-Network Implementation of the CDMA Multiuser Detector, p. 1257.

IEEE Transactions on Neural Networks, volume 10 (1999), number 6

• Wright, W.A., Bayesian Approach to Neural-Network Modeling with Input Uncertainty, pp. 1261-1270.
• Lu, B.-L., H. Kita, and Y. Nishikawa, Inverting Feedforward Neural Networks Using Linear and Nonlinear Programming, pp. 1271-1290.
• Helmbold, D.P., J. Kivinen, and M.K. Warmuth, Relative Loss Bounds for Single Neurons, pp. 1291-1304.
• Gori, M., A. KŸchler, and A. Sperduti, On the Implementation of Frontier- to-Root Tree Automata in Recursive Neural Networks, pp. 1305-1314.
• Bax, E., Partition-Based and Sharp Uniform Error Bounds, pp. 1315-1320.
• Jin, L. and M.M. Gupta, Stable Dynamic Backpropagation Learning in Recurrent Neural Networks, pp. 1321-1334.
• Treadgold, N.K. and T.D. Gedeon, Exploring Constructive Cascade Networks, pp. 1335-1350.
• Yamauchi, K., N. Yamaguchi, and N. Ishii, Incremental Learning Methods with Retrieving of Interfered Patterns, pp. 1351-1365.
• Juang, J.-C., Stability Analysis of Hopfield-Type Neural Networks, pp. 1366-1374.
• Pakdaman, K., C.P. Malta, and C. Grotta-Ragazzo, Asymptotic Behavior of Irreducible Excitatory Networks of Analog Graded-Response Neurons, pp. 1375-1381.
• Chellapilla, K. and D.B. Fogel, Evolving Neural Networks to Play Checkers Without Relying on Expert Knowledge, pp. 1382-1391.
• Schmitz, G.P.J., C. Aldrich, and F.S. Gouws, ANN-DT: An Algorithm for Extraction of Decision Trees from Artificial Neural Networks, pp. 1392-1401.
• Poznyak, A.S., W. Yu, E.N. Sanchez, and J.P. Perez, Nonlinear Adaptive Trajectory Tracking Using Dynamic Neural Networks, pp. 1402-1411.
• Chu, Y.-C. and J. Huang, A Neural-Network Method for the Nonlinear Servomechanism Problem, pp. 1412-1423.
• Wilson, D.J.H., G.W. Irwin, and G. Lightbody, RBF Principal Manifolds for Process Monitoring, pp. 1424-1434.
• Mandic, D.P. and J.A. Chambers, Toward an Optimal PRNN-Based Nonlinear Predictor, pp. 1435-1442.
• Nie, J. and S. Haykin, A Dynamic Channel Assignment Policy Through Q- Learning, pp. 1443-1455.
• Edwards, P.J., A.F. Murray, G. Papadopoulos, A.R. Wallace, J. Barnard, and G. Smith, The Application of Neural Networks to the Papermaking Industry, pp. 1456-1464.
• Morns, I.P. and S.S. Dlay, The DSFPN, a New Neural Network for Optical Character Recognition, pp. 1465-1473.
• Lyhyaoui, A., M. Martinez, I. Mora, M. Vazquez, J.-L. Sancho, and A.R. Figueiras-Vidal, Sample Selection Via Clustering to Construct Support Vector-Like Classifiers, pp. 1474-1481.
• Leung, C.S., G.H. Young, J. Sum, and W. Kan, On the Regularization of Forgetting Recursive Least Square, pp. 1482-1486.
• Yi, Z., P.A. Heng, and A.W.C. Fu, Estimate of Exponential Convergence Rate and Exponential Stability for Neural Networks, pp. 1487-1493.
• Shukla, D., D.M. Dawson, and F.W. Paul, Multiple Neural-Network-Based Adaptive Controller Using Orthonormal Activation Function Neural Networks, pp. 1494-1501.
• Aires, F., M. Schmitt, A. Chedin, and N. Scott, The "Weight Smoothing" Regularization of MLP for Jacobian Stabilization, pp. 1502-1510.
• Wang, C. and J.C. Principe, Training Neural Networks with Additive Noise in the Desired Signal, pp. 1511-1517.
• Ali, ....G. and Y.-T. Chen, Design Quality and Robustness with Neural Networks, pp. 1518-1527.
• Pessoa, L. and A.P. Leita˜o, Complex Cell Prototype Representation for Face Recognition, pp. 1528-1530.
• Wšhler, C. and J.K. Anlauf, An Adaptable Time-Delay Neural-Network Algorithm for Image Sequence Analysis, pp. 1531-1535.
• Cho, S.-Y. and T.W.S. Chow, Shape Recovery from Shading by a New Neural- Based Reflectance Model, pp. 1536-1540.
• Oh, S.-H. and S.-Y. Lee, Corrections to "A New Error Functino at Hidden Layers for Fast Training of Multilayer Perceptrons, p. 1541.