Coactive neural fuzzy modeling. - Eiji Mizutani; Jyh-Shing Roger Jang (ICNN, 1995)

Coactive neuro-fuzzy modelling for colour recipe prediction. - Eiji Mizutani; Jyh-Shing Roger Jang; Kenichi Nishio; Hideyuki Takagi; David M. Auslander (ICNN, 1995)

Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]. - Jyh-Shing Roger Jang; Chuen-Tsai Sun; Eiji Mizutani (IEEE Trans. Autom. Control., 1997)

Powell's dogleg trust-region steps with the quasi-Newton augmented Hessian for neural nonlinear least-squares learning. - Eiji Mizutani (IJCNN, 1999)

Sample path-based policy-only learning by actor neural networks. - Eiji Mizutani (IJCNN, 1999)

On Derivation of MLP Backpropagation from the Kelley-Bryson Optimal-Control Gradient Formula and Its Application. - Eiji Mizutani; Stuart E. Dreyfus; Kenichi Nishio (IJCNN (2), 2000)

On Iterative Krylov-Dogleg Trust-Region Steps for Solving Neural Networks Nonlinear Least Squares Problems. - Eiji Mizutani; James Demmel (NIPS, 2000)

Evolving color recipes. - Eiji Mizutani; Hideyuki Takagi; David M. Auslander; Jyh-Shing Roger Jang (IEEE Trans. Syst. Man Cybern. Part C, 2000)

Fuzzy mixtures of complementary local experts: towards neuro-fuzzy modular networks. - Eiji Mizutani; Kenichi Nishio (FUZZ-IEEE, 2002)

Multi-illuminant color reproduction for electronic cameras via CANFIS neuro-fuzzy modular network device characterization. - Eiji Mizutani; Kenichi Nishio (IEEE Trans. Neural Networks, 2002)

On using discretized Cohen-Grossberg node dynamics for model-free actor-critic neural learning in non-Markovian domains. - Eiji Mizutani; Stuart E. Dreyfus (CIRA, 2003)

On sparsity-exploiting memory-efficient trust-region regularized nonlinear least squares algorithms for neural-network learning. - Eiji Mizutani; James Weldon Demmel (IJCNN, 2003)

Iterative Scaled Trust-Region Learning in Krylov Subspaces via Pearlmutter's Implicit Sparse Hessian-Vector Multiply. - Eiji Mizutani; James Demmel (NIPS, 2003)

On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning. - Eiji Mizutani; James Demmel (Neural Networks, 2003)

Two stochastic dynamic programming problems by model-free actor-critic recurrent-network learning in non-Markovian settings. - Eiji Mizutani; Stuart E. Dreyfus (IJCNN, 2004)

Stagewise Newton, differential dynamic programming, and neighboring optimum control for neural-network learning. - Eiji Mizutani; Stuart E. Dreyfus (ACC, 2005)

Second-order backpropagation algorithms for a stagewise-partitioned separable Hessian matrix. - Eiji Mizutani; Stuart E. Dreyfus; James Weldon Demmel (IJCNN, 2005)

On derivation of stagewise second-order backpropagation by invariant imbedding for multi-stage neural-network learning. - Eiji Mizutani; Stuart Dreyfus (IJCNN, 2006)

On practical use of stagewise second-order backpropagation for multi-stage neural-network learning. - Eiji Mizutani; Stuart Dreyfus (IJCNN, 2007)

On exploiting symmetry for multilayer perceptron learning. - Eiji Mizutani; Jing-Yun Carey Fan (IJCNN, 2007)

Second-order stagewise backpropagation for Hessian-matrix analyses and investigation of negative curvature. - Eiji Mizutani; Stuart E. Dreyfus (Neural Networks, 2008)

Corrigendum to: "Second-order stagewise backpropagation for Hessian-matrix analyses and investigation of negative curvature" [Neural Netw. 21(2-3) (2008) 193-203] - Eiji Mizutani; Stuart E. Dreyfus (Neural Networks, 2008)

An analysis on negative curvature induced by singularity in multi-layer neural-network learning. - Eiji Mizutani; Stuart Dreyfus (NIPS, 2010)

On exploiting sparsity in CANFIS neuro-fuzzy modular network learning by second-order stagewise backpropagation. - Eiji Mizutani (FSKD, 2011)

Online neuro-fuzzy CANFIS hidden-node teaching. - Eiji Mizutani; Jing-Yun Carey Fan (FUZZ-IEEE, 2011)

On improving trust-region variable projection algorithms for separable nonlinear least squares learning. - Eiji Mizutani; James Demmel (IJCNN, 2011)

A block-approximate local Hessian-matrix analysis for CANFIS neuro-fuzzy modular network learning. - Eiji Mizutani (FUZZ-IEEE, 2012)

On a staged successive approximation procedure for approximate dynamic programming solutions to board game playing. - Eiji Mizutani (IEEM, 2015)

On Pantoja's problem allegedly showing a distinction between differential dynamic programming and stagewise Newton methods. - Eiji Mizutani (Int. J. Control, 2015)

Totally model-free actor-critic recurrent neural-network reinforcement learning in non-Markovian domains. - Eiji Mizutani; Stuart E. Dreyfus (Ann. Oper. Res., 2017)

On Two New Dynamic-Programming Procedures As Efficient As the Wagner-Whitin Regeneration-Point Type in Dynamic Lot Sizing. - Eiji Mizutani; Brigitte Trista (IEEM, 2019)

A Note on Liao's Recurrent Neural-Network Learning for Discrete Multi-stage Optimal Control Problems. - Eiji Mizutani (Neural Process. Lett., 2019)

A Proof on Equivalence of Stagewise Newton and Dreyfus's Successive Approximation Procedures. - Eiji Mizutani (IEEE Trans. Autom. Control., 2020)

On using dynamic programming for time warping in pattern recognition. - Eiji Mizutani; Stuart Dreyfus (Inf. Sci., 2021)

A note on a single-shift days-off scheduling problem with sequence-dependent labor costs. - Eiji Mizutani; Kevin Alexander Sánchez Galeano (J. Sched., 2023)

A tutorial on the art of dynamic programming for some issues concerning Bellman's principle of optimality. - Eiji Mizutani; Stuart Dreyfus (ICT Express, 2023)

On Stage-Wise Backpropagation for Improving Cheng's Method for Fully Connected Cascade Networks. - Eiji Mizutani; Naoyuki Kubota; Tam Chi Truong (Neural Process. Lett., 2024)