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Adaptive Resonance Theory (ART) is one of the successful approaches to resolving “the plasticity–stability dilemma” in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes’ Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. [ABSTRACT FROM AUTHOR]

The decision making process in communication is affected by internal and external factors from dynamic environments. Humans can perform a variety of behaviors in a similar situation, unlike robots. This paper discusses human psychological phenomena during communication from the point of view of internal and external factors, such as perception, memory, and emotional information. Based on these, we introduce the personality affected robotic emotional model and the emotion affected associative memory model for the robot. We organize an interactive robot system to provide suitable decisions for the robot. Results from interactive communication experiments indicate that the robot is able to perform different actions based on internal and external factors. [ABSTRACT FROM AUTHOR]

Electricity is one of the most consumed commodities in the modern world. Electricity load prediction models are used to plan distribution operations to balance the equilibrium of demand and supply. This necessity has increased the number of recent research works. They employed several learning algorithms, such as support vector regression, to predict demands. However, these algorithms have high computational cost and too many user-defined parameters that directly impact their performance. Recently, randomization-based learning algorithms have been widely tested because they performed well at a lower cost. However, still, there was a main drawback: uncertainty in approximation and learning. This work employed a kernel trick to solve the uncertainty problem. A kernel with reservoir-state layers was used to solve the problem. The kernel reservoir-state layers from the echo state network not only transformed features into high-dimensional space, but also enhanced the forecasting ability by learning temporal information. Additionally, the proposed model also had a multi-step prediction ability that used previous forecasting errors to update the output weights in the current step to prevent an accumulated error problem. We compared our proposed model with single-layer and multi-layer variants of Extreme Learning Machine, Echo State Network, and Random Vector Functional Link on ten electrical load data sets. The proposed model showed the best performance on 9/10 data sets in terms of Mean Square Error or Symmetric Mean Absolute Percentage Error. These findings implied that the proposed algorithm was superior in forecasting long-term electricity load. • An error-output recurrent multi-step multi-layer kernel reservoir machine is proposed. • Solving an error accumulation problem in recurrent multi-step prediction. • The kernel trick is used to prevent uncertainty in approximation. • Employing the kernel reservoir states to learn temporal information. [ABSTRACT FROM AUTHOR]

• System design optimization with mixed subsystems failure dependencies is addressed. • System cost is minimized under availability constraint. • Three nature-inspired optimization techniques are implemented with constraint handling. • Results on a complex bridge network system and a parallel-series system are compared. Systems present dependencies among their components failure behavior, which impact their ultimate availability. Previous works addressed the optimal design of systems in relation to its cost and under given availability constraint, considering identical subsystems failure dependencies. The present paper addresses this problem in a realistic scenario by taking into consideration mixed subsystems failure dependencies. The problem is formulated with reference to a complex bridge network system and a series-parallel system. Three nature-inspired optimization techniques are implemented to solve the problem, namely differential evolution (DE), manta ray foraging optimization (MRFO), and shuffled frog leaping algorithm (SFLA) with constraint handling. A numerical evaluation is performed; the results show that DE outperforms MRFO and SFLA. [ABSTRACT FROM AUTHOR]