<![CDATA[ IEEE Transactions on Neural Networks and Learning Systems - new TOC ]]>
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TOC Alert for Publication# 5962385 2018February 15<![CDATA[Table of contents]]>292C1245113<![CDATA[IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information]]>292C2C285<![CDATA[Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks]]>confidence rules—and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural–symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.]]>2922462581701<![CDATA[An Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition]]>2922592722043<![CDATA[Hysteretic Noisy Chaotic Neural Networks for Resource Allocation in OFDMA System]]>2922732852696<![CDATA[Neuroadaptive Fault-Tolerant Control of Nonlinear Systems Under Output Constraints and Actuation Faults]]>2922862981325<![CDATA[State Estimation for Delayed Genetic Regulatory Networks With Reaction–Diffusion Terms]]>292299309853<![CDATA[Incomplete Multisource Transfer Learning]]>2923103232347<![CDATA[A pdf-Free Change Detection Test Based on Density Difference Estimation]]>2923243341884<![CDATA[Multisynchronization of Coupled Heterogeneous Genetic Oscillator Networks via Partial Impulsive Control]]>2923353421956<![CDATA[Concept Factorization With Adaptive Neighbors for Document Clustering]]>2923433522081<![CDATA[Multivariate Cryptography Based on Clipped Hopfield Neural Network]]>$text {GF}(p)$ space. The Diffie–Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.]]>2923533631677<![CDATA[Passivity and Output Synchronization of Complex Dynamical Networks With Fixed and Adaptive Coupling Strength]]>2923643761491<![CDATA[Constrained Null Space Component Analysis for Semiblind Source Separation Problem]]>2923773913836<![CDATA[Learning to Predict Eye Fixations via Multiresolution Convolutional Neural Networks]]>2923924042298<![CDATA[Network Unfolding Map by Vertex-Edge Dynamics Modeling]]>2924054183484<![CDATA[Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors]]>2924194261830<![CDATA[Investigating Echo-State Networks Dynamics by Means of Recurrence Analysis]]>$L_mathrm {max}$ , is highly correlated with the well-established maximal local Lyapunov exponent. This suggests that complexity measures based on RP diagonal lines distribution can quantify network stability. Finally, our analysis shows that all RQA measures fluctuate on the proximity of the so-called edge of stability, where an ESN typically achieves maximum computational capability. We leverage on this property to determine the edge of stability and show that our criterion is more accurate than two well-known counterparts, both based on the Jacobian matrix of the reservoir. Therefore, we claim that RPs and RQA-based analyses are valuable tools to design an ESN, given a specific problem.]]>2924274394424<![CDATA[CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification]]>2924404562483<![CDATA[Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems]]>2924574696375<![CDATA[Forward Stagewise Additive Model for Collaborative Multiview Boosting]]>2924704853344<![CDATA[Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization]]>$ell _{p}$ -norm analysis, we organize the cluster-specific kernel weights into a matrix and introduce a matrix-based extension of the $ell _{p}$ -norm for constraint enforcement. By casting the joint optimization problem as a problem of alternating optimization, we show how the cluster structure is gradually revealed and how the matrix-regularized kernel weights are obtained. A theoretical analysis of such a regularizer is performed using a Rademacher complexity bound, and complementary empirical experiments on real-world data sets demonstrate the effectiveness of our technique.]]>2924864992537<![CDATA[High-Dimensional Function Approximation With Neural Networks for Large Volumes of Data]]>2925005081422<![CDATA[IEEE Computational Intelligence Society Information]]>292C3C357<![CDATA[IEEE Transactions on Neural Networks information for authors]]>292C4C4131