English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A stochastic conjugate gradient multi-modulus algorithm based on global artificial fish swarm and genetic optimization DNA encoding sequences(GAFS-GDNASMMA) is proposed to solve the high computational loads, low convergence rate, and large mean square error(MSE) of the multi-modulus blind equalization algorithm(MMA). In this proposed algorithm, the optimal DNA coding sequence can be found and used as the initial optimal weight vector of the stochastic conjugate gradient MMA(SMMA) after decoding the optimization DNA sequences. The simulation results show that the proposed algorithm has the faster convergence speed and smaller mean square error comparison with MMA, SMMA, GA-DNA-SMMA(Genetic algorithm and DNA optimization based SMMA) Introduction In order to overcome the low convergence rate, high computational loads, and large mean square error(MSE) of the multi-modulus blind equalization algorithm(MMA), it is necessary to propose a novel multi-modulus blind equalization algorithm. Therefore, the conjugate gradient method with quadratic convergence rate is introduced into the MMA to quickly find the optimal solution. The stochastic conjugate gradient MMA(SMMA) can reduce the amount of calculation greatly and still has faster convergence speed and small mean square error comparison with the multi-modulus blind equalization algorithm(MMA). The DNA genetic algorithm(DNA-GA) has the flexible encoding and higher encoding accuracy. Besides, it has the ability of global search. The global artificial fish swarm algorithm (GAFSA) is a new and highly efficient intelligence algorithm with the global searching ability and can overcome the defects of the low precision, slow convergence, and high complexity of the basic artificial fish swarm algorithm. So, the DNA-GA fusing into GAFSA can improve the ability of global search and find the best DNA sequence quickly. In this paper, the stochastic conjugate gradient multi-modulus blind equalization algorithm based on global artificial fish swarm and genetic optimization DNA encoding sequence(GAFSGDNASMMA) is proposed via fusing the advantages of the MMA, the DNA-GA, and the GAFSA. DNA Genetic Algorithm Based on GASFA The global artificial fish swarm algorithm(GAFSA) is an efficient optimal algorithm with preying, swarming, and following behavior of fish swarm. In a watering place, the fish can find the place which has a lot of food through the preying, swarming and following behavior. The artificial fish swarm can achieve the purpose of optimization according to the feature of the fish swarm. The DNA genetic algorithm(DNA-GA) has the advantages of traditional genetic algorithm. Besides, it is a good optimization method with the flexible encoding and high precision. In crossover operator, two individuals in the population are selected as the parent bodies. One sequence, which belongs to one parent body, is randomly selected. These two sequences have the same length and are exchanged to come into being two new individuals. In mutation operator, each base of each individual in the group can mutate into another base with International Industrial Informatics and Computer Engineering Conference (IIICEC 2015) © 2015. The authors Published by Atlantis Press 131 a certain probability to form a new population. Therefore, the GAFSA is fused with DNA-GA and carried out. DNA-GA with global artificial fish swarm algorithm is proposed to improve the ability of the global search and find the optimal value quickly and accurately. Novel Multi-Modulus Blind Equalization Algorithm The stochastic conjugate gradient multi-modulus algorithm(SMMA) adopts the conjugate gradient method in the updating process of the weight vector. The updating direction of every step in the updating process is composed of the gradient direction of the current iterative point and the searching direction of the previous iterative point. New searching direction including the gradient information of the current and previous points can reflect the current searching direction and make the equalizer coefficient converge to the global optimal point. The cost function of the SMMA is defined as 2 2 2 2 | ( ) | ( ) | ( ) | ( ) ( ) 2 2 2 2 p p p p R R R I I I SCGMMA y n y n R y n y n R J n E p p + +   = − + −   + +   (1) where 2 p = , p R R and p I R are the real and imaginary parts of modulus, respectively. ( ) R y n and ( ) I y n is the quadrature and in-phase component of the decision output signals. According to Eq.(1), the equalizer tap coefficient vector of the SMMA is updated by ( ) ( ) ( 1 ) W W d n n n μ + = + (2) , ( ) ( 1 ( 1 ) ) , 1 ( ) 1 ( ) g d g d n n n n n n n β =  =  + − − >  (3) [ ( )( | ( ) | ( ) ( )( | ( ) | ) ( ) ) ] g X p p p p R R R I I I n y n R y n jy n R y n n ∗ = − + − (4)

A Blind Equalization Algorithm Based on Global Artificial Fish Swarm and Genetic Optimization DNA Encoding Sequences