scholarly journals Hybrid Artificial Neural Network with Extreme Learning Machine Method and Cuckoo Search Algorithm to Predict Stock Prices

2020 ◽  
Vol 1 (2) ◽  
pp. 131
Author(s):  
Piping Prabawati ◽  
Auli Damayanti ◽  
Herry Suprajitno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Extreme Learning Machine ◽  
Stock Prices ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Training Process ◽  
Validation Test ◽  
Learning Machine

This thesis aims to predict the stock prices, using artificial neural network with extreme learning machine (ELM) method and cuckoo search algorithm (CSA). Stock is one type of investment that is in great demand in Indonesia. The portion ownership of stock is determined by how much investment is invested in the company. In this case, stock is an aggressive type of investment instrument, because stock prices can change over time. In this case, ELM is used to determine forecasting values, while CSA is applied to compile and optimize the values of weights and biases to be used in the forecasting process. After obtaining the best weights and biases, the validation test process is then carried out to determine the level of success of the training process. The data used is the daily data of the stock price of PT. Bank Mandiri (Persero) Tbk. the total is 291 data. Furthermore, the data is divided into 70% for the training process is as many as 199 data and 30% for the validation test as many as 87 data. Then compiled pattern of training and validation test patterns is 198 patterns and 82 patterns. Based on the implementation of the program, with several parameter obtained the result of  MSE training is 0.001304353, with an MSE of validation test is 0.0031517704. Because the MSE value obtained is relatively small, this indicates that the ELM-CSA network is able to recognize data patterns and is able to predict well.

Reproducing Polynomial Kernel Extreme Learning Machine

2017 ◽  
Vol 21 (5) ◽  
pp. 795-802 ◽  
Author(s):  
Yibo Li ◽  
Chao Liu ◽  
Senyue Zhang ◽  
Wenan Tan ◽  
Yanyan Ding ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Reproducing Kernel ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Fast Training ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Conventional kernel support vector machine (KSVM) has the problem of slow training speed, and single kernel extreme learning machine (KELM) also has some performance limitations, for which this paper proposes a new combined KELM model that build by the polynomial kernel and reproducing kernel on Sobolev Hilbert space. This model combines the advantages of global and local kernel function and has fast training speed. At the same time, an efficient optimization algorithm called cuckoo search algorithm is adopted to avoid blindness and inaccuracy in parameter selection. Experiments were performed on bi-spiral benchmark dataset, Banana dataset, as well as a number of classification and regression datasets from the UCI benchmark repository illustrate the feasibility of the proposed model. It achieves the better robustness and generalization performance when compared to other conventional KELM and KSVM, which demonstrates its effectiveness and usefulness.

scholarly journals A hybrid control strategy based on CSA and ELM for uncertain nonlinear chaotic system

2020 ◽  
Vol 14 ◽  
pp. 174830262092250
Author(s):  
Yan Li ◽  
Yigang He ◽  
Wenxin Yu
Keyword(s):  
Extreme Learning Machine ◽  
Chaotic System ◽  
Control Strategy ◽  
Chaotic Systems ◽  
Hybrid Control ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Nonlinear Chaotic System ◽  
Learning Machine

The study of nonlinear chaotic systems and their control is an important topic. In this paper, a hybrid control strategy based on cuckoo search algorithm and extreme learning machine is proposed. Cuckoo search algorithm is used in a hybrid control strategy in order to optimise the weights and biases in extreme learning machine leading to the improvement of its performance. Simulations indicate that the proposed method is able to fit nonlinear chaotic systems and control chaotic systems effectively. Data used in the nonlinear chaotic system are also tested for uncertainty and unknown systems. Simulation results confirm that the proposed method shows robustness for noisy data and perturbed parameters.

scholarly journals A Hybrid Model of Extreme Learning Machine Based on Bat and Cuckoo Search Algorithm for Regression and Multiclass Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qinwei Fan ◽  
Tongke Fan
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Classification Problems ◽  
Neural Network Learning ◽  
Practical Applications ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM), as a new simple feedforward neural network learning algorithm, has been extensively used in practical applications because of its good generalization performance and fast learning speed. However, the standard ELM requires more hidden nodes in the application due to the random assignment of hidden layer parameters, which in turn has disadvantages such as poorly hidden layer sparsity, low adjustment ability, and complex network structure. In this paper, we propose a hybrid ELM algorithm based on the bat and cuckoo search algorithm to optimize the input weight and threshold of the ELM algorithm. We test the numerical experimental performance of function approximation and classification problems under a few benchmark datasets; simulation results show that the proposed algorithm can obtain significantly better prediction accuracy compared to similar algorithms.

scholarly journals Hybrid Artificial Neural Network with Meta-heuristics for Grid-Connected Photovoltaic System Output Prediction

2018 ◽  
Vol 11 (1) ◽  
pp. 121
Author(s):  
Norfarizani Nordin ◽  
Shahril Irwan Sulaiman ◽  
Ahmad Maliki Omar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Search Algorithm ◽  
Computation Time ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Photovoltaic System ◽  
Ann Model ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

This paper presents the performance evaluation of hybrid Artificial Neural Network (ANN) model with selected meta-heuristics for predicting the AC output power fof a Grid-Connected Photovoltaic (GCPV). The ANN has been hybridized with three meta-heuristics, i.e. Cuckoo Search Algorithm (CSA), Evolutionary Programming (EP) and Firefly Algorithm (FA) separately. These meta-heuristics were used to optimize the number of neurons, learning rate and momentum rate such that the Root Mean Square Error (RMSE) of the prediction was minimized during the ANN training process. The results showed that CSA had outperformed EP and FA in producing the lowest RMSE. Later, Mutated Cuckoo Search Algorithm (MCSA) was introduced by incorporating Gaussian mutation operator in the conventional CSA. Further investigation showed that MSCA performed better prediction when compared with the conventional CSA in terms of RMSE and computation time.

scholarly journals A Real-time BOD Estimation Method in Wastewater Treatment Process Based on an Optimized Extreme Learning Machine

2019 ◽  
Vol 9 (3) ◽  
pp. 523 ◽  
Author(s):  
Ping Yu ◽  
Jie Cao ◽  
Veeriah Jegatheesan ◽  
Xianjun Du
Keyword(s):  
Wastewater Treatment ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Bp Network ◽  
Learning Machine ◽  
Hidden Layer ◽  
Improved Cuckoo Search Algorithm

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy, and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

Sign in / Sign up

Export Citation Format

Share Document