Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization

Das, Gyanesh; Pattnaik, Prasant Kumar; Padhy, Sasmita Kumari

doi:10.1016/j.eswa.2013.10.053

Cited by 167 publications

(91 citation statements)

References 24 publications

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“…Particle swarm optimization (PSO) was first introduced by Kennedy and Eberhart, which is an efficient optimization technique simulating the social behavior, e.g., bird flocking and fish training [37]. An initial population randomly generated was called particles (solutions).…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

Ruan

Shi

et al. 2017

Applied Sciences

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Abstract:The commercially available nanoscale zerovalent zinc (nZVZ) was used as an adsorbent for the removal of malachite green (MG) from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration) and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO) and artificial neural network coupled with genetic algorithm (ANN-GA) were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12%) was compatible with the experimental value (90.72%). Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (∆G 0 , ∆H 0 and ∆S 0 ) were calculated from the Van't Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

Ruan

Shi

et al. 2017

Applied Sciences

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“…The training of the ANN is carried out by applying an iterative optimization process to minimize the MSE by updating the weights and biases appropriately [14]. The MSE is defined as the error between the expected output and actual outputs, given as:…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Jànos [13] applied a global optimization framework for parameterization of ANNs. Das et al [14] applied an ANN trained with PSO to the problem of channel equalization. Although some of the global search techniques have been applied to optimize weights of ANNs, these algorithms may still converge to the local optimum solution due to lack of exploitation capability.…”

Section: Introductionmentioning

confidence: 99%

Speech recognition using ANN and predator-influenced civilized swarm optimization algorithm

Mittal¹,

Sharma²

2016

Turk J Elec Eng & Comp Sci

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This paper proposes a hybrid optimization technique, predator-influenced civilized swarm optimization, by integrating civilized swarm optimization (CSO) and predator-prey optimization (PPO) techniques. CSO is the integration of the attributes of particle swarm optimization and a society civilization algorithm (SCA). In the SCA, the swarm is divided into a few societies, and each society has its own society leader (SL); other individuals of the society are termed society members. The combination of all such societies forms a civilization, and the best-performing SL becomes the civilization leader (CL). In CSO, SLs and members update their positions through the guidance of their own CL and SLs, respectively, along with their best positions. In the proposed technique, the PPO technique is integrated with CSO, in which a predator particle is included in the swarm. The predator always tries to chase the CL in a controlled manner, which maintains diversity in the population and avoids local optimum solutions. The proposed optimization technique is applied to optimize the weights and biases of an artificial neural network (ANN) trained for speech recognition. Two databases have been used; one is a TI-46 isolated word database in clean and noisy conditions, and the other is a self-recorded Hindi numeral database. To evaluate the performance of the proposed technique, 2 performance criteria, correlation coefficient and mean square error, are applied. The results obtained by an ANN with the proposed technique outperform the results obtained by an ANN trained with particle swarm optimization, PPO, CSO, and backpropagation techniques in terms of correlation coefficient and mean square error.

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“…Despite the advantages of BPNN, it has some drawbacks that the most important one being their poor trainability. It might fall to local minima and cause overfitting and failure of the network training [34,35]. There is a recent trend to train BPNN with bio-inspired optimization algorithms for different applications [36,37,38].…”

Section: Introductionmentioning

confidence: 99%

ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

Karami

2015

Journal of Network and Computer Applications

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Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based Internet infrastructure. However, NDN is subject to congestion when the number of data packets that reach one or various routers in a certain period of time is so high than its queue gets overflowed. To address this problem many congestion control protocols have been proposed in literature which, however, they are highly sensitive to their control parameters as well as unable to predict congestion traffic well enough in advance. This paper develops an Adaptive Congestion Control Protocol in NDN (ACCPndn) by learning capacities in two phases to control congestion traffics before they start impacting the network performance. In the first phase -adaptive trainingwe propose a Time-Lagged Feedforward Network (TLFN) optimized by hybridization of particle swarm optimization and genetic algorithm to predict the source of congestion together with the amount of congestion. In the second phase -fuzzy avoidance-we employ a non-linear fuzzy logic-based control system to make a proactive decision based on the outcomes of first phase in each router per interface to control and/or prevent packet drop well enough in advance. Extensive simulations and results show that ACCPndn sufficiently satisfies the applied performance metrics and outperforms two previous proposals such as NACK and HoBHIS in terms of the minimal packet drop and highutilization (retrying alternative paths) in bottleneck links to mitigate congestion traffics.

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Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization

Cited by 167 publications

References 24 publications

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

Speech recognition using ANN and predator-influenced civilized swarm optimization algorithm

ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

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