AIMC Topic: Computer Simulation

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A Novel Adaptive Linear Neuron Based on DNA Strand Displacement Reaction Network.

IEEE/ACM transactions on computational biology and bioinformatics
Analog DNA strand displacement circuits can be used to build artificial neural network due to the continuity of dynamic behavior. In this study, DNA implementations of novel catalysis, novel degradation and adjustment reaction modules are designed an...

Finite-frequency control for nonlinear semi-Markov jump systems with piecewise transition probabilities.

ISA transactions
Considering the frequency effect of external disturbances, this paper concerns the finite-frequency control problem for nonlinear semi-Markov jump systems (SMJSs) with piecewise transition probabilities (TPs) via the Takagi-Sugeno (T-S) fuzzy modelin...

Approximation properties of Gaussian-binary restricted Boltzmann machines and Gaussian-binary deep belief networks.

Neural networks : the official journal of the International Neural Network Society
Despite the successful use of Gaussian-binary restricted Boltzmann machines (GB-RBMs) and Gaussian-binary deep belief networks (GB-DBNs), little is known about their theoretical approximation capabilities to represent distributions of continuous rand...

A Path-Planning Approach Based on Potential and Dynamic Q-Learning for Mobile Robots in Unknown Environment.

Computational intelligence and neuroscience
The path-planning approach plays an important role in determining how long the mobile robots can travel. To solve the path-planning problem of mobile robots in an unknown environment, a potential and dynamic Q-learning (PDQL) approach is proposed, wh...

Reachable Set Estimation of Delayed Markovian Jump Neural Networks Based on an Improved Reciprocally Convex Inequality.

IEEE transactions on neural networks and learning systems
This brief investigates the reachable set estimation problem of the delayed Markovian jump neural networks (NNs) with bounded disturbances. First, an improved reciprocally convex inequality is proposed, which contains some existing ones as its specia...

Multi-Swarm Algorithm for Extreme Learning Machine Optimization.

Sensors (Basel, Switzerland)
There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which mak...

Deep Learning for Joint Pilot Design and Channel Estimation in MIMO-OFDM Systems.

Sensors (Basel, Switzerland)
In MIMO-OFDM systems, pilot design and estimation algorithm jointly determine the reliability and effectiveness of pilot-based channel estimation methods. In order to improve the channel estimation accuracy with less pilot overhead, a deep learning s...

AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network.

Scientific reports
Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient machine learning model for fast intrusion detection and prevention ...

Commercial Bank Credit Grading Model Using Genetic Optimization Neural Network and Cluster Analysis.

Computational intelligence and neuroscience
Commercial banks are facing unprecedented credit risk challenges as the financial market becomes more volatile. Based on this, this study proposes and builds a credit risk assessment model for commercial banks based on GANN from the standpoint of com...

Design of Sports Training Simulation System for Children Based on Improved Deep Neural Network.

Computational intelligence and neuroscience
With the development of AI technology, human-computer interaction technology is no longer the traditional mouse and keyboard interaction. AI and VR have been widely used in early childhood education. In the process of the slow development and applica...