Design and implementation of sharp edge FIR filters using hybrid differential evolution particle swarm optimization
The filter is an important building block of modern communication and electronic systems.
Based on well-defined bandwidth, it extracts the desired portion of the spectrum when the …
Based on well-defined bandwidth, it extracts the desired portion of the spectrum when the …
Benchmarking studies aimed at clustering and classification tasks using K-means, fuzzy C-means and evolutionary neural networks
A Pickens, S Sengupta - Machine Learning and Knowledge Extraction, 2021 - mdpi.com
Clustering is a widely used unsupervised learning technique across data mining and
machine learning applications and finds frequent use in diverse fields ranging from …
machine learning applications and finds frequent use in diverse fields ranging from …
Fast converging cuckoo search algorithm to design symmetric FIR filters
Elimination of noise from transmitted signals inevitably incorporated during transmission
persisted important task for the researchers from the preliminary days of Signal Processing …
persisted important task for the researchers from the preliminary days of Signal Processing …
Hardware efficient FIR filter design using global best steered quantum inspired cuckoo search algorithm
In this paper, a new algorithm namely Global Best Steered Quantum Inspired Cuckoo
Search Algorithm (GQICSA) is proposed for obtaining optimized set of coefficients to …
Search Algorithm (GQICSA) is proposed for obtaining optimized set of coefficients to …
Qdds: A novel quantum swarm algorithm inspired by a double dirac delta potential
In this paper a novel Quantum Double Delta Swarm (QDDS) algorithm modeled after the
mechanism of convergence to the center of attractive potential field generated within a …
mechanism of convergence to the center of attractive potential field generated within a …
Chaotic quantum double delta swarm algorithm using chebyshev maps: Theoretical foundations, performance analyses and convergence issues
The Quantum Double Delta Swarm (QDDS) Algorithm is a networked, fully-connected novel
metaheuristic optimization algorithm inspired by the convergence mechanism to the center …
metaheuristic optimization algorithm inspired by the convergence mechanism to the center …
Learning to track on-the-fly using a particle filter with annealed-weighted QPSO modeled after a singular Dirac delta potential
S Sengupta, RA Peters - 2018 IEEE Symposium Series on …, 2018 - ieeexplore.ieee.org
This paper proposes an evolutionary Particle Filter with a memory guided proposal step size
update and an improved, fully-connected Quantum-behaved Particle Swarm Optimization …
update and an improved, fully-connected Quantum-behaved Particle Swarm Optimization …
Benchmarking Clustering and Classification Tasks using K-Means, Fuzzy C-Means and Feedforward Neural Networks optimized by PSO
A Pickens - 2021 - digitalcommons.murraystate.edu
Clustering is a widely used unsupervised learning technique across data mining and
machine learning applications and finds frequent use in diverse fields ranging from …
machine learning applications and finds frequent use in diverse fields ranging from …
QDDS–A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives
S Sengupta - 2019 - search.proquest.com
With sensor fusion and data-driven approaches taking center stage in ubiquitous computing,
customized and application-specific optimization methods are increasingly important. The …
customized and application-specific optimization methods are increasingly important. The …