AIMC Topic: Systems Theory

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Learning in deep neural networks and brains with similarity-weighted interleaved learning.

Proceedings of the National Academy of Sciences of the United States of America Jun 27, 2022
Understanding how the brain learns throughout a lifetime remains a long-standing challenge. In artificial neural networks (ANNs), incorporating novel information too rapidly results in catastrophic interference, i.e., abrupt loss of previously acquir...
Humans Models, Neurological Systems Theory Neocortex Neural Networks, Computer Learning
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A complementary learning systems approach to temporal difference learning.

Neural networks : the official journal of the International Neural Network Society Oct 26, 2019
Complementary Learning Systems (CLS) theory suggests that the brain uses a 'neocortical' and a 'hippocampal' learning system to achieve complex behaviour. These two systems are complementary in that the 'neocortical' system relies on slow learning of...
Humans Systems Theory Systems Analysis Algorithms Neural Networks, Computer Learning Hippocampus Reinforcement, Psychology
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DynMat, a network that can learn after learning.

Neural networks : the official journal of the International Neural Network Society Apr 8, 2019
To survive in the dynamically-evolving world, we accumulate knowledge and improve our skills based on experience. In the process, gaining new knowledge does not disrupt our vigilance to external stimuli. In other words, our learning process is 'accum...
Systems Theory Humans Algorithms Brain Neural Networks, Computer Unsupervised Machine Learning
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The stability of memristive multidirectional associative memory neural networks with time-varying delays in the leakage terms via sampled-data control.

PloS one Sep 24, 2018
In this paper, we propose a new model of memristive multidirectional associative memory neural networks, which concludes the time-varying delays in leakage terms via sampled-data control. We use the input delay method to turn the sampling system into...
Humans Brain Computer Simulation Neural Networks, Computer Mathematical Concepts Nerve Net Time Factors Systems Theory Memory Models, Neurological Association Models, Psychological
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Resilience of and recovery strategies for weighted networks.

PloS one Sep 11, 2018
The robustness and resilience of complex networks have been widely studied and discussed in both research and industry because today, the diversity of system components and the complexity of the connection between units are increasingly influencing t...
Systems Theory Models, Theoretical Computer Communication Networks Neural Networks, Computer Computer Simulation
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A New Correntropy-Based Conjugate Gradient Backpropagation Algorithm for Improving Training in Neural Networks.

IEEE transactions on neural networks and learning systems May 10, 2018
Mean square error (MSE) is the most prominent criterion in training neural networks and has been employed in numerous learning problems. In this paper, we suggest a group of novel robust information theoretic backpropagation (BP) methods, as correntr...
Humans Neural Networks, Computer Artificial Intelligence Information Storage and Retrieval Pattern Recognition, Automated Systems Theory Algorithms Models, Neurological
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A symbolic network-based nonlinear theory for dynamical systems observability.

Scientific reports Feb 28, 2018
When the state of the whole reaction network can be inferred by just measuring the dynamics of a limited set of nodes the system is said to be fully observable. However, as the number of all possible combinations of measured variables and time deriva...
Systems Theory DNA Replication Neural Networks, Computer Algorithms Nonlinear Dynamics Observation Humans Models, Theoretical
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A New Hybrid BFOA-PSO Optimization Technique for Decoupling and Robust Control of Two-Coupled Distillation Column Process.

Computational intelligence and neuroscience Oct 11, 2016
The two-coupled distillation column process is a physically complicated system in many aspects. Specifically, the nested interrelationship between system inputs and outputs constitutes one of the significant challenges in system control design. Mostl...
Distillation Search Engine Systems Theory Robotics Algorithms Heuristics Computer Simulation Models, Theoretical Humans
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Structure Identification of Uncertain Complex Networks Based on Anticipatory Projective Synchronization.

PloS one Oct 7, 2015
This paper investigates a method to identify uncertain system parameters and unknown topological structure in general complex networks with or without time delay. A complex network, which has uncertain topology and unknown parameters, is designed as ...
Nonlinear Dynamics Uncertainty Computer Simulation Neural Networks, Computer Algorithms Systems Theory Time Factors
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Robust stability of stochastic fuzzy delayed neural networks with impulsive time window.

Neural networks : the official journal of the International Neural Network Society Mar 27, 2015
The urgent problem of impulsive moments which cannot be determined in advance brings new challenges beyond the conventional impulsive systems theory. In order to solve this problem, the novel concept of impulsive time window is proposed in this paper...
Nonlinear Dynamics Fuzzy Logic Systems Theory Stochastic Processes Uncertainty Models, Neurological Neural Networks, Computer Algorithms
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