Simple binary coded genetic algorithm (GA) and particle swarm optimization (PSO) fall easily into local minimums and fail to find the global optimal solution to the algorithm. Thus, the development of a hybrid algorithm between GA and PSO is urgently demanded. In this paper, a three-layer polygonal fuzzy neural network (PFNN) model and its error function are first given by the arithmetic operations of the polygonal fuzzy numbers. Second, the random sequences are constructed by a chaos random generator, these random sequences are used as the initial population of chaos GA and the optimal individuals for sub-populations gained by chaos search are used as the initial population of PSO, and then an new parallel conjugate gradient-particle swarm optimization (PCG-PSO) is designed. Finally, a case study shows the proposed parallel CG-PS algorithm not only avoids dependence of traditional GA on initial values and overcomes the poor global optimization capability of traditional PSO, but also possesses advantages of rapid convergence and high stability.
J.J.Buckley and Y.Hayashi, Fuzzy neural networks: A survey, Fuzzy Sets and Systems66(1) 1994, 1–13.
2.
J.J.Buckley and Y.Hayashi, Can fuzzy neural nets approximate continuous fuzzy function, Fuzzy Sets and Systems61(1) 1994, 43–51.
3.
R. and A.Aliev, Genetic algorithm-based learning of fuzzy neural network, International Journal of Approximate Reasoning1182001351–358.
4.
R.A.Aliev, B.G.Guirimov and R.R.Aliev, et al., Evolutionary algorithm-based learning of fuzzy neural networks, Part 2: Recurrent fuzzy neural networks, Fuzzy Sets and Systems160(17) 2009, 2553–2566.
5.
J.R.Castro, O.Castillo and P.Melin, et al., A hybrid learning algo-rithm for a class of interval type-2 fuzzy neural networks, Information Sciences179(13) 2009, 2175–2193.
6.
K.Dahala, K.Almejallib and M.A.Hossainc, GA-based learning for rule identification in fuzzy neural networks, Applied Soft Computing352015, 605–617.
7.
P.Y.Liu, A new fuzzy neural network and its approximation performance, Science China (Series E)32(1) 2002, 76–86.
8.
P.Y.Liu, Fuzzy Neural Network Theory and Applications [D], Beijing: Beijing Normal University, 2002.
9.
D.L.Lang and Z.M.Liu, The misadjustment analysis of BP algorithm and improved algorithm, Acta Electronica Sinica23(1) 1995, 117–120.
10.
C.M.He and Y.P.Ye, Evolution computation based learning algorithms of polygonal fuzzy neural networks, International Journal of Intelligent Systems26(4) 2011, 340–352.
11.
G.J.Wang and X.P.Li, Universal approximation of polygonal fuzzy neural networks in sense of K-integral norms, Science China, Information Science54(11) 2011, 2307–2323.
12.
Y.He and G.J.Wang, The conjugate gradient algorithm of the polygonal fuzzy neural networks, Acta Electronica Sinica 40(10) 2012, 2079–2084.
13.
X.L.Sui and G.J.Wang, Influence of perturbations of training pattern pairs on stability of polygonal fuzzy neural network, Pattern Recognition and Artificial Intelligence26(6) 2012, 928–936.
14.
G.J.Wang, Y.He and X.P.Li, Optimization algorithms for MISO polygonal fuzzy neural networks, Science China Information Science45(5) 2015, 650–667.
15.
G.J.Wang and X.P.Li, Construction of the polygonal fuzzy neural network and its approximation based on K-integral norm, Neural Network World24(4) 2014, 357–376.
16.
X.P.Li and D.Li, The structure and realization of a polygonal fuzzy neural network, International Journal of Machine Learning and Cybernetics7(3) 2016, 375–389.
17.
G.J.Wang and C.F.Suo, The isolation layered optimization algorithm of MIMO polygonal fuzzy neural network,Neural Computing and Applications29(10) 2018, 721–731.
18.
X.H.Zhang, G.Z.Dai and N.P.Xu, Genetic algorithms-a new optimization and search algorithms, Control Theory and Application12(3) 1995, 265–273.
19.
J.H.Zhang and X.H.Xu, A new evolutionary algorithm-ant colony algorithm, Systems Engineering Theory and Practice19(3) 1999, 84–87.
20.
H.B.Gao and L.Gao, Particle swarm optimization based algorithm for neural network learning, Acta Electronica Sinica32(9) 2004, 1572–1574.
21.
Y.L.Lee, A.A.Ei-saleh and M.Iamail, Gravity-based particle swarm optimization with hybrid cooperative swarm approach for global optimization, Journal of Intelligent & Fuzzy Systems26(1) 2014, 465–481.
22.
H.Melo and J.Watada, Gaussian-PSO with fuzzy reasoning based on structural learning for training a neural network, Neurocomputing1722016, 405–412.
23.
B.Hu, J.S.Yang and L.B.Wang, Improved chaos genetic algorithm and its application, Computer Engineering36(5) 2010, 170–172.
24.
X.H.Yuan, Y.B.Yuan and C.Wang, et al., A novel self-adaptive chaotic genetic algorithm, Acta Electronica Sinica34(4) 2006, 708–712.
25.
T.B.Wu, Y.Cheng and T.Y.Zhou, et al., Optimization control of PID based on chaos genetic algorithm, Journal of computer simulation26(5) 2009, 202–204.
26.
F.Wang, Y.S.Dai and S.S.Wang, Modified chaos-genetic algorithm, Computer Engineering and Applications46(6) 2010, 29–32.
27.
S.H.Xu and X.G.He, A training algorithm of process neural networks based on CGA combined with PSO, Control and Decision28(9) 2013, 1393–1398.
28.
Z.W.Zhao, G.Q.Ni and Y.Y.Shen, et al., Multiple multidimensional fuzzy reasoning algorithm based on neural network, Journal of Intelligent & Fuzzy Systems35(4) 2018, 4121–4129.
29.
Y.Q.Yang, G.J.Wang and Y.Yang, Parameters optimization of polygonal fuzzy neural networks based on GA-BP hybrid algorithm, International Journal of Machine Learning and Cybernetics5(5) 2014, 815–822.
30.
L.A.Zadeh, Fuzzy sets, Information and Control81965, 338–353.
31.
M.S.A.Khan, S.Abdullah and A.Ali, et al., Pythagorean hesitant fuzzy sets and their application to group decision making with incomplete weight information, Journal of Intelligent & Fuzzy Systems33(6) 2017, 3971–3985.
32.
M.S.A.Khan, A.Ali and S.Abdullah, et al., New extension of TOPSIS method based on Pythagorean hesitant fuzzy sets with incomplete weight information, Journal of Intelligent & Fuzzy Systems35(5) 2018, 5435–5448.
33.
Q.B.Luo and J.Zhang, A new approach to generate chaotic pseudo-random sequence, Journal of Electronics and Information Technology28(7) 2006, 1262–1264.
34.
Z.G.Cheng, L.Q.Zhang and X.L.Li, Chaotic hybrid particle swarm optimization algorithm based on tent map, Systems Engineering and Electronics29(1) 2007, 103–106.
35.
J.L.Fan and X.F.Zhang, Piecewise logistic chaotic map and its performance analysis, Acta Electronica Sinica37(4) 2009, 720–725.
36.
F.Han and J.S.Zhu, Improved particle swarm optimization combined with backpropagation for feedforward neural networks, International Journal of Intelligent Systems28(3)(2013), 271–288.
37.
D.Peralta, I.Triguero and S.García, et al., On the use of convolutional neural netwoks for robust classification of multiple fingerprint captures, International Journal of Intelligent Systems33(1) 2018, 213–230.
38.
Y.H.Duan, Research of predictive fuzzy-PID controller in temperature of central air-conditioning system, Journal of System Simulation20(3) 2008, 600–606.
39.
J.Q.Cao and K.Xu, Variable universe self-adaptive fuzzy PID control applied in the VAV air-conditioning systems, Journal of Computer Simulation28(5) 2011, 197–200.
40.
D.G.Wang, C.L.P.Chen, W.Y.Song and H.X.Li, Error compensated marginal linearization method for modeling a fuzzy system, IEEE Transactions on Fuzzy Systems23(1) 2015, 215–222.
41.
D.G.Wang, W.Y.Song and H.X.Li, Approximation properties of ELM-fuzzy systems for smooth functions and their derivatives, Neurocomputing1492015, 265–274.
