IEEE Transactions on Neural Networks, volume 9 (1998), number 1

• Pakdaman, K. and C.P. Malta, A note on convergence under dynamical thresholds with delays, pp. 231-233.
• Card, H.C., Doubly stochastic poisson processes in artificial neural learning, pp. 229-231.
• Huang, G.B. and H.A. Babri, Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions, pp. 224-229.
• Hou, C.H. and J.X. Qian, Stability analysis for neural dynamics with time- varying delays, pp. 221-223.
• Lebaron, B. and A.S. Weigend, A bootstrap evaluation of the effect of data splitting on financial time series, pp. 213-220.
• Fels, S.S. and G.E. Hinton, Glove-talkii - a neural-network interface which maps gestures to parallel formant speech synthesizer controls, pp. 205-212.
• Healy, M.J. and T.P. Caudell, Guaranteed two-pass convergence for supervised and inferential learning, pp. 195-204.
• Wang, J. and Y.S. Xia, Analysis and design of primal-dual assignment networks, pp. 183-194.
• Diamantini, C. and A. Spalvieri, Quantizing for minimum average misclassification risk, pp. 174-182.
• Malthouse, E.C., Limitations of nonlinear pca as performed with generic neural networks, pp. 165-173.
• Kane, J.S., A smart pixel-based feedforward neural network, pp. 159- 164.
• Fu, L.M., Learning in certainty-factor-based multilayer neural networks for classification, pp. 151-158.
• Fernandezdelgado, M. and S.B. Ameneiro, Mart - a multichannel art-based neural network, pp. 139-150.
• Malouche, Z. and O. Macchi, Adaptive unsupervised extraction of one component of a linear mixture with a single neuron, pp. 123-138.
• Matsuyama, Y., Multiple descent cost competition - restorable self- organization and multimedia information processing, pp. 106-122.
• Zhang, Y.Q. and A. Kandel, Compensatory neurofuzzy systems with fast learning algorithms, pp. 83-105.
• Ghorbani, A.A. and V.C. Bhavsar, Incremental communication for multilayer neural networks - error analysis, pp. 68-82.
• Chen, T.P., Y.B. Hua, and W.Y. Yan, Global convergence of ojas subspace algorithm for principal component extraction, pp. 58-67.
• Schultz, A. and H. Wechsler, A discrete dynamics model for synchronization of pulse-coupled oscillators, pp. 51-57.
• Burshtein, D., Long-term attraction in higher order neural networks, pp. 42-50.
• Leisch, F., L.C. Jain, and K. Hornik, Cross-validation with active pattern selection for neural-network classifiers, pp. 35-41.
• Noriega, J.R. and H. Wang, A direct adaptive neural-network control for unknown nonlinear systems and its application, pp. 27-34.
• Ma, S. and C.Y. Ji, Fast training of recurrent networks based on the em algorithm, pp. 11-26.
• Coultrip, R., A cmos binary pattern classifier based on parzens method, pp. 2-10.

IEEE Transactions on Neural Networks, volume 9 (1998), number 2

• Qi, X.F. and F. Palmieri, Comments on theoretical analysis of evolutionary algorithms with an infinite population size in continuous space - part i - basic properties of selection and mutation - reply, pp. 342-343.
• Gao, Y., Comments on theoretical analysis of evolutionary algorithms with an infinite population size in continuous space - part i - basic properties of selection and mutation, pp. 341-342.
• Vanmilligen, B.P., V. Tribaldos, J.A. Jimenez, and C.S. Cruz, Comments on an accelerated learning algorithm for multilayer perceptrons - optimization layer by layer, pp. 339-341.
• Martinez, D., Neural tree density estimation for novelty detection, pp. 330-338.
• Chatterjee, C., V.P. Roychowdhury, and E.K.P. Chong, On relative convergence properties of principal component analysis algorithms, pp. 319-329.
• Lu, Y.W., N. Sundararajan, and P. Saratchandran, Performance evaluation of a sequential minimal radial basis function (rbf) neural network learning algorithm, pp. 308-318.
• Wang, Y.J. and C.T. Lin, Runge-kutta neural network for identification of dynamical systems in high accuracy, pp. 294-307.
• Ritter, G.X., P. Sussner, and J.L. Diazdeleon, Morphological associative memories, pp. 281-293.
• Yuan, J.L. and T.L. Fine, Neural-network design for small training sets of high dimension, pp. 266-280.
• Rae, R. and H.J. Ritter, Recognition of human head orientation based on artificial neural networks, pp. 257-265.
• Krzyzak, A. and T. Linder, Radial basis function networks and complexity regularization in function learning, pp. 247-256.
• Bernardini, A. and S. Defina, Optimal decision boundaries for m-qam signal formats using neural classifiers, pp. 241-246.

IEEE Transactions on Neural Networks, volume 9 (1998), number 3

• Ruf, B. and M. Schmitt, Self-organization of spiking neurons using action potential timing, pp. 575-578.
• Gori, M., M. Maggini, E. Martinelli, and G. Soda, Inductive inference from noisy examples using the hybrid finite state filter, pp. 571-575.
• Bebis, G., M. Georgiopoulos, and N.D. Lobo, Using self-organizing maps to learn geometric hash functions for model-based object recognition, pp. 560-570.
• Frank, T., K.F. Kraiss, and T. Kuhlen, Comparative analysis of fuzzy art and art-2a network clustering performance, pp. 544-559.
• Sweatman, C.Z.W.H., B. Mulgrew, and G.J. Gibson, Two algorithms for neural-network design and training with application to channel equalization, pp. 533-543.
• Linsay, P.S. and D.L.L. Wang, Fast numerical integration of relaxation oscillator networks based on singular limit solutions, pp. 523-532.
• Raghu, P.P. and B. Yegnanarayana, Supervised texture classification using a probabilistic neural network and constraint satisfaction model, pp. 516-522.
• Miller, D.A. and J.M. Zurada, A dynamical system perspective of structural learning with forgetting, pp. 508-515.
• Amerijckx, C., M. Verleysen, P. Thissen, and J.D. Legat, Image compression by self-organized kohonen map, pp. 503-507.
• Chandrasekaran, V. and Z.Q. Liu, Topology constraint free fuzzy gated neural networks for pattern recognition, pp. 483-502.
• Weaver, S., L. Baird, and M.M. Polycarpou, An analytical framework for local feedforward networks, pp. 473-482.
• Dotan, Y. and N. Intrator, Multimodality exploration by an unsupervised projection pursuit neural network, pp. 464-472.
• Miao, X., M.R. Azimisadjadi, B. Tian, A.C. Dubey, and N.H. Witherspoon, Detection of mines and minelike targets using principal component and neural-network methods, pp. 454-463.
• Zhou, G. and J. Si, Advanced neural-network training algorithm with reduced complexity based on jacobian deficiency, pp. 448-453.
• Diamantaras, K.I. and M.G. Strintzis, Neural classifiers using one-time updating, pp. 436-447.
• Chon, K.H., N.H. Holsteinrathlou, D.J. Marsh, and V.Z. Marmarelis, Comparative nonlinear modeling of renal autoregulation in rats - volterra approach versus artificial neural networks, pp. 430-435.
• Lu, S.W. and T. Basar, Robust nonlinear system identification using neural-network models, pp. 407-429.
• Khan, J.I., Characteristics of multidimensional holographic associative memory in retrieval with dynamically localizable attention, pp. 389- 406.
• Deleone, R., R. Capparuccia, and E. Merelli, A successive overrelaxation backpropagation algorithm for neural-network training, pp. 381-388.
• Song, H.H. and S.W. Lee, A self-organizing neural tree for large-set pattern classification, pp. 369-380.
• Sundareshan, M.K. and T.A. Condarcure, Recurrent neural-network training by a learning automaton approach for trajectory learning and control system design, pp. 354-368.
• Shiratani, F. and K. Yamamoto, Deterministic annealing techniques for a discrete-time neural-network updating in a block-sequential mode, pp. 345-353.

IEEE Transactions on Neural Networks, volume 9 (1998), number 4

• Dundar, G. and K. Rose, Comments on the effects of quantization on multilayer neural networks - authors reply, p. 719.
• Kwon, O.J. and S.Y. Bang, Comments on the effects of quantization on multilayer neural networks, pp. 718-719.
• Wang, L.P. and K. Smith, On chaotic simulated annealing, pp. 716-718.
• Huang, G.B. and H.A. Babri, Comments on approximation capability in c((r)over-bar(n)) by multilayer feedforward networks and related problems, pp. 714-715.
• Sommer, F.T. and P. Dayan, Bayesian retrieval in associative memories with storage errors, pp. 705-713.
• Wang, L.P., Effects of noise in training patterns on the memory capacity of the fully connected binary hopfield neural network - mean-field theory and simulations, pp. 697-704.
• Neubauer, C., Evaluation of convolutional neural networks for visual recognition, pp. 685-696.
• Mcloone, S., M.D. Brown, G. Irwin, and G. Lightbody, A hybrid linear/nonlinear training algorithm for feedforward neural networks, pp. 669-684.
• Treadgold, N.K. and T.D. Gedeon, Simulated annealing and weight decay in adaptive learning - the sarprop algorithm, pp. 662-668.
• Ruiz, A., D.H. Owens, and S. Townley, Existence, learning, and replication of periodic motions in recurrent neural networks, pp. 651-661.
• Ormoneit, D. and V. Tresp, Averaging, maximum penalized likelihood and bayesian estimation for improving gaussian mixture probability density estimates, pp. 639-650.
• Teixeira, M.C.M. and S.H. Zak, Analog neural nonderivative optimizers, pp. 629-638.
• Brause, R.W. and M. Rippl, Noise suppressing sensor encoding and neural signal orthonormalization, pp. 613-628.
• Pedrycz, W., Conditional fuzzy clustering in the design of radial basis function neural networks, pp. 601-612.
• Fierro, R. and F.L. Lewis, Control of a nonholonomic mobile robot using neural networks, pp. 589-600.
• Kwan, C.M., F.L. Lewis, and D.M. Dawson, Robust neural-network control of rigid-link electrically driven robots, pp. 581-588.

IEEE Transactions on Neural Networks, volume 9 (1998), number 5

• Gori, M. and A.C. Tsoi, Comments on local minima free conditions in multilayer perceptrons, pp. 1051-1053.
• Lin, C.L., Control of perturbed systems using neural networks, pp. 1046-1050.
• Liao, X.F. and J.B. Yu, Robust stability for interval hopfield neural networks with time delay, pp. 1042-1045.
• Kartalopoulos, S.V., An associative ram-based cam and its application to broad-band communications systems, pp. 1036-1041.
• Eltoft, T. and R.J.P. Defigueiredo, A new neural network for cluster- detection-and-labeling, pp. 1021-1035.
• Farrell, J.A., Stability and approximator convergence in nonparametric nonlinear adaptive control, pp. 1008-1020.
• Farrell, J.A., On performance evaluation in on-line approximation for control, pp. 1001-1007.
• Lagaris, I.E., A. Likas, and D.I. Fotiadis, Artificial neural networks for solving ordinary and partial differential equations, pp. 987-1000.
• Sellami, L. and R.W. Newcomb, An inverse hollis-paulos artificial neural network, pp. 979-986.
• Maiorov, V. and R.S. Meir, Approximation bounds for smooth functions in c(ird) by neural and mixture networks, pp. 969-978.
• Sun, F.C., Z.Q. Sun, and P.Y. Woo, Stable neural-network-based adaptive control for sampled-data nonlinear systems, pp. 956-968.
• Man, Z.H., H.R. Wu, and M. Palaniswami, An adaptive tracking controller using neural networks for a class of nonlinear systems, pp. 947-955.
• Olurotimi, O. and S. Das, Noisy recurrent neural networks - the discrete- time case, pp. 937-946.
• Das, S. and O. Olurotimi, Noisy recurrent neural networks - the continuous-time case, pp. 913-936.
• Yen, J.C., J.I. Guo, and H.C. Chen, A new k-winners-take-all neural network and its array architecture, pp. 901-912.
• Deodhare, D., M. Vidyasagar, and S.S. Keerthi, Synthesis of fault-tolerant feedforward neural networks using minimax optimization, pp. 891-900.
• Petridis, V. and V.G. Kaburlasos, Fuzzy lattice neural network (flnn) - a hybrid model for learning, pp. 877-890.
• Petridis, V., E. Paterakis, and A. Kehagias, Hybrid neural-genetic multimodal parameter estimation algorithm, pp. 862-876.
• Meneganti, M., F.S. Saviello, and R. Tagliaferri, Fuzzy neural networks for classification and detection of anomalies, pp. 848-861.
• Machado, R.J., V.C. Barbosa, and P.A. Neves, Learning in the combinatorial neural model, pp. 831-847.
• Kumar, R. and P. Rockett, Multiobjective genetic algorithm partitioning for hierarchical learning of high-dimensional pattern spaces - a learning- follows-decomposition strategy, pp. 822-830.
• Kubat, M., Decision trees can initialize radial-basis function networks, pp. 813-821.
• Kodjabachian, J. and J.A. Meyer, Evolution and development of neural controllers for locomotion, gradient-following, and obstacle-avoidance in artificial insects, pp. 796-812.
• Fu, L.M., A neural-network model for learning domain rules based on its activation function characteristics, pp. 787-795.
• Frasconi, P., M. Gori, and A. Sperduti, A general framework for adaptive processing of data structures, pp. 768-786.
• Farag, W.A., V.H. Quintana, and G. Lamberttorres, A genetic-based neuro- fuzzy approach for modeling and control of dynamical systems, pp. 756-767.
• Chan, S.W.K. and J. Franklin, Symbolic connectionism in natural language disambiguation, pp. 739-755.
• Baraldi, A., P. Blonda, F. Parmiggiani, G. Pasquariello, and G. Satalino, Model transitions in descending flvq, pp. 724-738.
• Giles, C.L., R. Sun, and J.M. Zurada, Neural networks and hybrid intelligent models - foundations, theory, and applications, pp. 721- 723.

IEEE Transactions on Neural Networks, volume 9 (1998), number 6

• Tickle, A.B., R. Andrews, M. Golea, and J. Diederich, The Truth Will Come to Light: Directions and Challenges in Extracting the Knowledge Embedded Within Trained Artificial Neural Networks, pp. 1057-1068.
• Turchetti, C., M. Conti, P. Crippa, and S. Orcioni, On the Approximation of Stochastic Processes by Approximate Identity Neural Networks, pp. 1069-1085.
• Gori, M., F. Scarselli, and A.C. Tsoi, On the Closure of the Set of Functions That Can be Realized by a Given Multilayer Perceptron, pp. 1086-1098.
• Wang, G. and H. Shi, TMLNN: Triple-Valued or Multiple-Valued Logic Neural Network, pp. 1099-1117.
• Cotüofanaˇ, S. and S. Vassiliadis, Periodic Symmetric Functions, Serial Addition, and Multiplication with Neural Networks, pp. 1118- 1128.
• Hush, D.R. and B. Horne, Efficient Algorithms for Function Approximation with Piecewise Linear Sigmoidal Networks, pp. 1129-1141.
• Pal, N.R. and V.K. Eluri, Two Efficient Connectionist Schemes for Structure Preserving Dimensionality Reduction, pp. 1142-1154.
• Webb, A.R. and Simon Shannon, Shape-Adaptive Radial Basis Functions, pp. 1155-1166.
• Filho, B.D. Baptista, E.L.L. Cabral, and A.J. Soares, A New Approach to Artificial Neural Networks, pp. 1167-1179.
• Young, S. and T. Downs, CARVE--A Constructive Algorithm for Real-Valued Examples, pp. 1180-1190.
• Takahashi, H. and H. Gu, A Tight Bound on Concept Learning, pp. 1191- 1202.
• Banerjee, M., S. Mitra, and S.K. Pal, Rough Fuzzy MLP: Knowledge Encoding and Classification, pp. 1203-1216.
• Sun, R. and T. Peterson, Autonomous Learning of Sequential Tasks: Experiments and Analyzes, pp. 1217-1234.
• Chu, W.C. and N.K. Bose, Vector Quantization of Neural Networks, pp. 1235-1245.
• Delmas, J.-P. and J.-F. Cardos, Asymptotic Distributions associated to Oja's Learning Equation for Neural Networks, pp. 1246-1257.
• Choy, C. S.-T. and W.-C. Siu, A Class of Competitive Learning Models which Avoids Neuron Underutilization Problem, pp. 1258-1269.
• Haese, K., Self-Organizing Feature Maps with Self-Adjusting Learning Parameters, pp. 1270-1278.
• Heinke, D. and F.H. Hamker, Comparing Neural Networks: A Benchmark on Growing Neural Gas, Growing Cell Structures, and Fuzzy ARTMAP, pp. 1279-1291.
• Wang, G. and N. Ansari, Searching for Optimal Frame Patterns in an Integrated TDMA Communication System Using Mean Field Annealing, pp. 1292-1300.
• Smith, K., M. Palaniswami, and M. Krishnamoorthy, Neural Techniques for Combinatorial Optimization with Applications, pp. 1301-1318.
• Matsuda, S., "Optimal" Hopfield Network for Combinatorial Optimization with Linear Cost Function, pp. 1319-1330.
• Xia, Y. and J. Wang, A General Methodology for Designing Globally Convergent Optimization Neural Networks, pp. 1331-1343.
• Perez-Ilzarbe, M.J., Convergence Analysis of a Discrete-Time Recurrent Neural Network to Perform Quadratic Real Optimization with Bound Constraints, pp. 1344-1351.
• Behnke, S. and N.B. Karayiannis, Competitive Neural Trees for Pattern Classification, pp. 1352-1369.
• Cruz, C. Santa and J.R. Dorronsoro, A Nonlinear Discriminant Algorithm for Feature Extraction and Data Classification, pp. 1370-1376.
• Wu, S. and K.Y. Michael Wong, Dynamic Overload Control for Distributed Call Processors Using the Neural-Network Method, pp. 1377-1387.
• Parisini, T. and R. Zoppoli, Neural Approximations for Infinite-Horizon Optimal Control of Nonlinear Stochastic Systems, pp. 1388-1408.
• He, S., K. Reif, and R. Unbehauen, A Neural Approach for Control of Nonlinear Systems with Feedback Linearization, pp. 1409-1421.
• Limanond, S. and J. Si, Neural-Network-Based Control Design: An LMI Approach, pp. 1422-1429.
• Chen, S.-H. and Y.-F. Liao, Modular Recurrent Neural Networks for Mandarin Syllable Recognition, pp. 1430-1441.
• You, C. and D. Hong, Nonlinear Blind Equalization Schemes Using Complex- Valued Multilayer Feedforward Neural Networks, pp. 1442-1455.
• Saad, E.W., Danil V. Prokhorov, and D.C. Wunsch, Comparative Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks, pp. 1456-1470.
• Geva, A.B., ScaleNet--Multiscale Neural-Network Architecture for Time Series Prediction, pp. 1471-1482.
• Raffo, L., S.P. Sabatini, G.M. Bo, and G.M. Bisio, Analog VLSI Circuits as Physical Structures for Perception in Early Visual Tasks, pp. 1483- 1494.
• Girolami, M., A. Cichocki, and S.-I. Amari, A Common Neural-Network Model for Unsupervised Exploratory Data Analysis and Independent Component Analysis, pp. 1495-1501.
• Yamada, S., M. Nakashima, and S. Shiono, Reinforcement Learning to Train a Cooperative Network with Both Discrete and Continuous Output Neurons, pp. 1502-1508.
• Gonzalez-Serrano, F.J., A.R. Figueiras-Vidal, and A. Artes-Rodriguez, Generalizing CMAC Architecture and Training, pp. 1509-1514.
• Chinnam, R.B. and J. Ding, Prediction Limit Estimation for Neural Network Models, pp. 1515-1522.
• Funabiki, N. and J. Kitamichi, A Gradual Neural-Network Algorithm for Jointly Time-Slot/Code Assignment Problems in Packet Radio Networks, pp. 1523-1528.
• Andersen, H.C., A. Lotfi, and L.C. Westphal, Comments on "Functional Equivalence Between Radial Basis Function Networks and Fuzzy Inference Systems", pp. 1529-1530.
• Jang, J.R., Author's Reply, p. 1531.
• Baraldi, A., P. Blonda, F. Parmiggiani, G. Pasquariello, and G. Satalino, Errata to "Model Transitions in Descending FLVQ", p. 1532.