Fuzzy knowledge combination. - Bart Kosko (Int. J. Intell. Syst., 1986)

Fuzzy Cognitive Maps. - Bart Kosko (Int. J. Man Mach. Stud., 1986)

Fuzzy entropy and conditioning. - Bart Kosko (Inf. Sci., 1986)

Counting with Fuzzy Sets. - Bart Kosko (IEEE Trans. Pattern Anal. Mach. Intell., 1986)

Feedback stability and unsupervised learning. - Bart Kosko (ICNN, 1988)

Hidden patterns in combined and adaptive knowledge networks. - Bart Kosko (Int. J. Approx. Reason., 1988)

Bidirectional associative memories. - Bart Kosko (IEEE Trans. Syst. Man Cybern., 1988)

Comparison of fuzzy and neural truck backer-upper control systems. - Seong-Gon Kong; Bart Kosko (IJCNN, 1990)

Unsupervised learning in noise. - Bart Kosko (IEEE Trans. Neural Networks, 1990)

Differential competitive learning for centroid estimation and phoneme recognition. - Seong-Gon Kong; Bart Kosko (IEEE Trans. Neural Networks, 1991)

Stochastic competitive learning. - Bart Kosko (IEEE Trans. Neural Networks, 1991)

Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. - Bart Kosko (Prentice Hall, 1992)

Fuzzy subimage classification in image sequence coding. - Seong-Gon Kong; Bart Kosko (ICASSP, 1992)

Adaptive fuzzy systems for backing up a truck-and-trailer. - Seong-Gon Kong; Bart Kosko (IEEE Trans. Neural Networks, 1992)

Fuzzy function learning with covariance ellipsoids. - Julie A. Dickerson; Bart Kosko (ICNN, 1993)

Addition as fuzzy mutual entropy. - Bart Kosko (Inf. Sci., 1993)

Virtual Worlds as Fuzzy Cognitive Maps. - Julie A. Dickerson; Bart Kosko (Presence Teleoperators Virtual Environ., 1994)

Fuzzy Systems as Universal Approximators. - Bart Kosko (IEEE Trans. Computers, 1994)

The probability monopoly. - Bart Kosko (IEEE Trans. Fuzzy Syst., 1994)

Optimal fuzzy rules cover extrema. - Bart Kosko (Int. J. Intell. Syst., 1995)

Adaptive fuzzy frequency hopper. - Peter J. Pacini; Bart Kosko (IEEE Trans. Commun., 1995)

Fuzzy throttle and brake control for platoons of smart cars. - Hyun Mun Kim; Julie A. Dickerson; Bart Kosko (Fuzzy Sets Syst., 1996)

Fuzzy prediction and filtering in impulsive noise. - Hyun Mun Kim; Bart Kosko (Fuzzy Sets Syst., 1996)

Fuzzy function approximation with ellipsoidal rules. - Julie A. Dickerson; Bart Kosko (IEEE Trans. Syst. Man Cybern. Part B, 1996)

Neural Fuzzy Agents that Learn a User's Preference Map. - Sanya Mitaim; Bart Kosko (ADL, 1997)

Neural Fuzzy Agents for Profile Learning and Object Matching. - Sanya Mitaim; Bart Kosko (Agents, 1997)

Neural Fuzzy Agents for Database Search. - Sanya Mitaim; Bart Kosko (CIA, 1997)

Neural fuzzy agents that learn profiles and search databases. - Sanya Mitaim; Bart Kosko (ICNN, 1997)

Adaptive joint fuzzy sets for function approximation. - Sanya Mitaim; Bart Kosko (ICNN, 1997)

Neural fuzzy motion estimation and compensation. - Hyun Mun Kim; Bart Kosko (IEEE Trans. Signal Process., 1997)

Stochastic Resonance with Adaptive Fuzzy Systems. - Sanya Mitaim; Bart Kosko (ICML, 1998)

Neural fuzzy stochastic resonance. - Sanya Mitaim; Bart Kosko (SMC, 1998)

Special Issue On Intelligent Signal Processing. - Simon Haykin 001; Bart Kosko (Proc. IEEE, 1998)

Adaptive stochastic resonance. - Sanya Mitaim; Bart Kosko (Proc. IEEE, 1998)

Neural Fuzzy Agents for Profile Learning and Adaptive Object Matching. - Sanya Mitaim; Bart Kosko (Presence Teleoperators Virtual Environ., 1998)

Global stability of generalized additive fuzzy systems. - Bart Kosko (IEEE Trans. Syst. Man Cybern. Part C, 1998)

The shape of fuzzy sets in adaptive function approximation. - Sanya Mitaim; Bart Kosko (IEEE Trans. Fuzzy Syst., 2001)

A conditioning interval based on superconditionals and superpower sets. - Bart Kosko (FUZZ-IEEE, 2002)

Almost all noise types can improve the mutual information of threshold neurons that detect subthreshold signals. - Bart Kosko; Sanya Mitaim (IJCNN, 2003)

Stochastic resonance in noisy threshold neurons. - Bart Kosko; Sanya Mitaim (Neural Networks, 2003)

Probable equivalence, superpower sets, and superconditionals. - Bart Kosko (Int. J. Intell. Syst., 2004)

Adaptive stochastic resonance in noisy neurons based on mutual information. - Sanya Mitaim; Bart Kosko (IEEE Trans. Neural Networks, 2004)

Noise benefits in spiking retinal and sensory neuron models. - Ashok Patel; Bart Kosko (IJCNN, 2005)

Stochastic resonance in noisy spiking retinal and sensory neuron models. - Ashok Patel; Bart Kosko (Neural Networks, 2005)

Modeling gunshot bruises in soft body armor with an adaptive fuzzy system. - Ian Lee; Bart Kosko; W. French Anderson (IEEE Trans. Syst. Man Cybern. Part B, 2005)

Mutual-Information Noise Benefits in Brownian Models of Continuous and Spiking Neurons. - Ashok Patel; Bart Kosko (IJCNN, 2006)

Levy Noise Benefits in Neural Signal Detection. - Ashok Patel; Bart Kosko (ICASSP (3), 2007)

Optimal noise benefits in Neyman-Pearson signal detection. - Ashok Patel; Bart Kosko (ICASSP, 2008)

Stochastic Resonance in Continuous and Spiking Neuron Models With Levy Noise. - Ashok Patel; Bart Kosko (IEEE Trans. Neural Networks, 2008)

Quantizer noise benefits in nonlinear signal detection with alpha-stable channel noise. - Ashok Patel; Bart Kosko (ICASSP, 2009)

Adaptive fuzzy priors for Bayesian inference. - Osonde Osoba; Sanya Mitaim; Bart Kosko (IJCNN, 2009)

Neural signal-detection noise benefits based on error probability. - Ashok Patel; Bart Kosko (IJCNN, 2009)

Error-probability noise benefits in threshold neural signal detection. - Ashok Patel; Bart Kosko (Neural Networks, 2009)

Optimal noise benefits in Neyman-Pearson and inequality-constrained statistical signal detection. - Ashok Patel; Bart Kosko (IEEE Trans. Signal Process., 2009)

Stochastic Resonance Noise Benefits in Markov Chain Monte Carlo Density Estimation. - Brandon Franzke; Bart Kosko (DMIN, 2010)

Optimal Mean-Square Noise Benefits in Quantizer-Array Linear Estimation. - Ashok Patel; Bart Kosko (IEEE Signal Process. Lett., 2010)

Triply fuzzy function approximation for Bayesian inference. - Osonde Osoba; Sanya Mitaim; Bart Kosko (IJCNN, 2011)

Noise benefits in the expectation-maximization algorithm: Nem theorems and models. - Osonde Osoba; Sanya Mitaim; Bart Kosko (IJCNN, 2011)

Bayesian Inference With Adaptive Fuzzy Priors and Likelihoods. - Osonde Osoba; Sanya Mitaim; Bart Kosko (IEEE Trans. Syst. Man Cybern. Part B, 2011)

Noise Benefits in Quantizer-Array Correlation Detection and Watermark Decoding. - Ashok Patel; Bart Kosko (IEEE Trans. Signal Process., 2011)

Triply fuzzy function approximation for hierarchical Bayesian inference. - Osonde Osoba; Sanya Mitaim; Bart Kosko (Fuzzy Optim. Decis. Mak., 2012)

Noise benefits in backpropagation and deep bidirectional pre-training. - Kartik Audhkhasi; Osonde Osoba; Bart Kosko (IJCNN, 2013)

Noisy hidden Markov models for speech recognition. - Kartik Audhkhasi; Osonde Osoba; Bart Kosko (IJCNN, 2013)

Noise-enhanced clustering and competitive learning algorithms. - Osonde Osoba; Bart Kosko (Neural Networks, 2013)

Corrigendum to "Noise enhanced clustering and competitive learning algorithms" [Neural Networks 37 (2013) 132-140]. - Osonde Osoba; Bart Kosko (Neural Networks, 2013)

Using Noise to Speed up Markov Chain Monte Carlo Estimation. - Brandon Franzke; Bart Kosko (INNS Conference on Big Data, 2015)

Additive Fuzzy Systems as Generalized Probability Mixture Models. - Bart Kosko (Fifty Years of Fuzzy Logic and its Applications, 2015)

Noise-enhanced convolutional neural networks. - Kartik Audhkhasi; Osonde Osoba; Bart Kosko (Neural Networks, 2016)

Using noise to speed up video classification with recurrent backpropagation. - Olaoluwa Adigun; Bart Kosko (IJCNN, 2017)

Generalized mixture representations and combinations for additive fuzzy systems. - Bart Kosko (IJCNN, 2017)

Training Generative Adversarial Networks with Bidirectional Backpropagation. - Olaoluwa Adigun; Bart Kosko (ICMLA, 2018)

Noisy Expectation-Maximization: Applications and Generalizations. - Osonde Osoba; Bart Kosko (CoRR, 2018)

Additive Fuzzy Systems: From Generalized Mixtures to Rule Continua. - Bart Kosko (Int. J. Intell. Syst., 2018)

Beyond DAGs: Modeling Causal Feedback with Fuzzy Cognitive Maps. - Osonde Osoba; Bart Kosko (CoRR, 2019)

Noise-boosted bidirectional backpropagation and adversarial learning. - Olaoluwa Adigun; Bart Kosko (Neural Networks, 2019)

Convergence of Generalized Probability Mixtures That Describe Adaptive Fuzzy Rule-based Systems. - Bart Kosko (FUZZ-IEEE, 2020)

High Capacity Neural Block Classifiers with Logistic Neurons and Random Coding. - Olaoluwa Adigun; Bart Kosko (IJCNN, 2020)

Random Fuzzy-Rule Foams for Explainable AI. - Akash Kumar Panda; Bart Kosko (NAFIPS, 2020)

Noise can speed backpropagation learning and deep bidirectional pretraining. - Bart Kosko; Kartik Audhkhasi; Osonde Osoba (Neural Networks, 2020)

Bidirectional Backpropagation. - Olaoluwa Adigun; Bart Kosko (IEEE Trans. Syst. Man Cybern. Syst., 2020)

Bayesian Pruned Random Rule Foams for XAI. - Akash Kumar Panda; Bart Kosko (FUZZ-IEEE, 2021)

Bidirectional Backpropagation for High-Capacity Blocking Networks. - Olaoluwa Adigun; Bart Kosko (ICMLA, 2021)

Deeper Neural Networks with Non-Vanishing Logistic Hidden Units: NoVa vs. ReLU Neurons. - Olaoluwa Adigun; Bart Kosko (ICMLA, 2021)

Bayesian Bidirectional Backpropagation Learning. - Olaoluwa Adigun; Bart Kosko (IJCNN, 2021)

Uniform Mixture Convergence of Continuously Transformed Fuzzy Systems. - Bart Kosko (NAFIPS, 2021)

Bidirectional Associative Memories: Unsupervised Hebbian Learning to Bidirectional Backpropagation. - Bart Kosko (IEEE Trans. Syst. Man Cybern. Syst., 2021)

Erratum to "Bidirectional Associative Memories: Unsupervised Hebbian Learning to Bidirectional Backpropagation". - Bart Kosko (IEEE Trans. Syst. Man Cybern. Syst., 2021)

Uniform Convergence of Probability Mixtures that Represent Combined Fuzzy Systems. - Bart Kosko (FUZZ-IEEE, 2022)

Deeper Bidirectional Neural Networks with Generalized Non-Vanishing Hidden Neurons. - Olaoluwa Adigun; Bart Kosko (ICMLA, 2022)

Bayesian Rule Ontologies For XAI Classification and Regression. - Akash Kumar Panda; Bart Kosko (ICMLA, 2022)

Bidirectional Backpropagation Autoencoding Networks for Image Compression and Denoising. - Olaoluwa Adigun; Bart Kosko (ICMLA, 2023)

Hidden Priors for Bayesian Bidirectional Backpropagation. - Olaoluwa Adigun; Bart Kosko (SMC, 2023)

Noise-boosted recurrent backpropagation. - Olaoluwa Adigun; Bart Kosko (Neurocomputing, 2023)

Training Deep Neural Classifiers with Soft Diamond Regularizers. - Olaoluwa Adigun; Bart Kosko (ICMLA, 2024)

Bidirectional Variational Autoencoders. - Bart Kosko; Olaoluwa Adigun (IJCNN, 2024)

Controlled Causal Hallucinations Can Estimate Phantom Nodes in Multiexpert Mixtures of Fuzzy Cognitive Maps. - Akash Kumar Panda; Bart Kosko (CoRR, 2025)