Multiuser detection based on modified PSO algorithm for synchronous CDMA systems

Chang, Jhih-Chung

doi:10.1007/s00521-012-0988-0

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…The PSO algorithm shares many basic principles with genetic algorithm to compute quickly the problems. PSO with a stochastic global search feature, was initially developed for optimization of the constraint non‐convex problems as well as it was inspired using manner of animal swarms so that has been applied for neural and neuro‐fuzzy network training and solving of nonlinear problems (Chang, ; Karakuzu, Karakaya, & Cavuslu, ). The PSO is one of the most usually utilized evolutionary variants in hybrid techniques because of its capability of searching global optimum, convergence speed, and simplicity.…”

Section: Introductionmentioning

confidence: 99%

Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying

Taghinezhad

Kaveh

Jahanbakhshi

et al. 2020

J Food Process Engineering

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In the study, drying process of quince fruit was accomplished in a microwave‐convective dryer (MCD). The experiments were carried out at microwave power levels of 100, 200, and 300 W, air temperatures of 40, 55, and 70°C, and air velocities of 0.5, 1, and 1.5 m/s. Nevertheless, three artificial intelligence techniques consisted of artificial neural networks (ANNs), particle swarm optimizer (PSO), and grey wolf optimizer (GWO) were evaluated to predict the parameters of Deff, SEC, ΔE, and Sb. In the evaluation the data by ANNs, input parameters of networks consisted the values of air temperature, microwave power, and air velocity. According to the results, the maximum values of effective moisture diffusivity ( Deff) and specific energy consumption (SEC) were 1.71 × 10−9 m2/s and 126.07 kWh/kg, respectively. In addition, minimum values of total change in color (ΔE) and shrinkage ( Sb) of quince achieved 10.85 and 33.85%, respectively. For predicting all parameters, three models used in the study represented good predictive capability with R2 > 0.97. The obtained results showed that the GWO model had better predictive performance than the ANN and PSO models. Practical Application Drying food and agricultural products by application of microwave‐hot air blend dryers can be a good alternative to hot air and microwave dryers. Microwave energy infiltrates the product and facilitates heat release from the product and thus reduces drying time compared to single dryers. The main aim of applying such different models, mathematical simulation or modeling in the drying technology of agricultural products is to transform physical qualities and their interactions into numerical quantities and mathematical relationships.

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Section: Introductionmentioning

confidence: 99%

Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying

Taghinezhad

Kaveh

Jahanbakhshi

et al. 2020

J Food Process Engineering

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“…And, the positive acceleration constants ϕ 1 and ϕ 2 represent the weighting of the stochastic acceleration terms that pull each parti- and r 2 (h) are M-dimensional vectors consisting of independent random numbers uniformly distributed between 0 and 1, which are used to stochastically vary the relative pull of p n (h) and p g (h) to simulate the unpredictable component of natural swarm behavior. The inertia weight w(h) based on the linear decreasing strategy is considered critically for the convergence behavior of PSO (Chang 2013), which is selected to decrease during the optimization process. Thus, PSO tends to have more global search ability at the beginning of the run while having more local search ability near the end of the optimization.…”

Section: The Pso-based Estimatormentioning

confidence: 99%

“…where h max is the maximum number of iterations, w max and w min are chosen as 0.9 and 0.4, respectively, in this work as in Chang (2013). Because the PSO particle represents a series of priorities that range from −0.5 to 0.5.…”

Section: The Pso-based Estimatormentioning

confidence: 99%

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CFO estimation based on Taylor MVDR approach using particle swarm optimization for interleaved OFDMA uplink systems

Chang

2014

Soft Comput

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The problem of carrier frequency offset (CFO) estimation based on the Taylor minimum variance distortionless response (MVDR) criterion in interleaved orthogonal frequency division multiple access uplink systems was investigated. However, in the presence of large CFOs, more iteration is required for the iterative search technique. Therefore, a new CFO vector, based on the Taylor series expansion of the vector initially given, is proposed. The problem of finding the new CFO vector is formulated as the closed form of a generalized eigenvalue problem, which is readily solved. A two-stage method for increasing the accuracy of the residual CFO estimation featuring a low computational load is presented in this paper. A proper initial CFO estimate is required for the Taylor MVDR estimator. First, an initial CFO estimate is determined using a particle swarm optimization estimator. The predominant CFO estimate is then sent to the Taylor MVDR estimator to form an estimate. The proposed estimator can estimate CFOs with a lower computational load. Several computer simulation results are provided to illustrate the effectiveness of the proposed estimation approach.Keywords Carrier frequency offset · Minimum variance distortionless response · Orthogonal frequency division multiple access · Particle swarm optimization Communicated by V. Loia.

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A robust adaptive array beamformer using particle swarm optimization for space–time code division multiple access systems

Chang¹

2014

Information Sciences

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Multiuser detection based on modified PSO algorithm for synchronous CDMA systems

Cited by 3 publications

References 11 publications

Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying

Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying

CFO estimation based on Taylor MVDR approach using particle swarm optimization for interleaved OFDMA uplink systems

A robust adaptive array beamformer using particle swarm optimization for space–time code division multiple access systems

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