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This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation. CIM is a generalized similarity measurement which can maintain a high-noise reduction ability even in a high-dimensional space. In addition, a Growing Neural Gas (GNG)-based topology construction process is integrated into the ART framework to enhance its self-organizing ability. The simulation experiments with synthetic and real-world datasets show that the proposed model has an outstanding stable self-organizing ability for various test environments.

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The application of multiagent system (MAS) is becoming increasing popular as it allows agents in a system to pool resources together to achieve a common objective. A vital part of the MAS is the teamwork cooperation through the sharing of information and resources among the agents to optimize their efforts in accomplishing given objectives. A critical part of the teamwork effort is the ability to trust each other when executing any task to ensure efficient and successful cooperation. This paper presents the development of a trust estimation model that could empirically evaluate the trust of an agent in MAS. The proposed model is developed using temporal difference learning by incorporating the concept of Markov games and heuristics to estimate trust. Simulation experiments are conducted to test and evaluate the performance of the developed model against some of the recently reported model in the literature. The simulation experiments indicate that the developed model performs better in terms of accuracy and efficiency in estimating trust.

Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.

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