Enhancement Processing Time and Accuracy Training via Significant Parameters in the Batch BP Algorithm

The batch back prorogation algorithm is anew style for weight updating. The drawback of the BBP algorithm is its slow learning rate and easy convergence to the local minimum. The learning rate and momentum factor are the are the most significant parameter for increasing the efficiency of the BBP algorithm. We created the dynamic learning rate and dynamic momentum factor for increasing the efficiency of the algorithm. We used several data set for testing the effects of the dynamic learning rate and dynamic momentum factor that we created in this paper. All the experiments for both algorithms were performed on Matlab 2016 a. The stop training was determined ten power -5. The average accuracy training is 0.9909 and average processing time improved of dynamic algorithm is 430 times faster than the BBP algorithm. From the experimental results, the dynamic algorithm provides superior performance in terms of faster training with highest accuracy training compared to the manual algorithm. The dynamic parameters which created in this paper helped the algorithm to escape the local minimum and eliminate training saturation, thereby reducing training time and the number of epochs. The dynamic algorithm was achieving a superior level of performance compared with existing works (latest studies).

[1]  Mohammad Shukri Salman,et al.  Back-propagation algorithm with variable adaptive momentum , 2016, Knowl. Based Syst..

[2]  Fatma Susilawati Mohamad,et al.  A Review on Enhancements to Speed up Training of the Batch Back Propagation Algorithm , 2016 .

[3]  Hiroshi Hasegawa,et al.  Training Artificial Neural Network Using Modification of Differential Evolution Algorithm , 2015 .

[4]  Nazri Mohd Nawi,et al.  Enhancing Back Propagation Neural Network Algorithm with Adaptive Gain on Classification Problems , 2011 .

[5]  Madhuri A. Chaudhari,et al.  Conjugate gradient back-propagation based artificial neural network for real time power quality assessment , 2016 .

[6]  Shan Sung Liew,et al.  An optimized second order stochastic learning algorithm for neural network training , 2015, Neurocomputing.

[7]  Yun Pan,et al.  Optimizing neural network classification by using the Cuckoo algorithm , 2015, 2015 11th International Conference on Natural Computation (ICNC).

[8]  Elsadek Hussien Ibrahim,et al.  Improving Error Back Propagation Algorithm by using Cross Entropy Error Function and Adaptive Learning Rate , 2017 .

[9]  Huailin Zhao,et al.  Handwritten Digit Recognition Based on Improved BP Neural Network , 2017 .

[10]  Hamed Azami,et al.  A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[11]  Jun Wang,et al.  Exponent back propagation neural network forecasting for financial cross-correlation relationship , 2016, Expert Syst. Appl..

[12]  Nazri Mohd Nawi,et al.  The 4 th International Conference on Electrical Engineering and Informatics ( ICEEI 2013 ) A New Levenberg Marquardt Based Back Propagation Algorithm Trained with Cuckoo Search , 2013 .

[13]  Jian-Huai Cai,et al.  A kind of BP neural network algorithm based on grey interval , 2011, Int. J. Syst. Sci..

[14]  Tao Zhang,et al.  Predicating photovoltaic power generation using an improved hybrid heuristic method , 2016, 2016 Sixth International Conference on Information Science and Technology (ICIST).

[15]  Hai-Jun Rong,et al.  Adaptive backstepping control for magnetic bearing system via feedforward networks with random hidden nodes , 2016, Neurocomputing.

[16]  Leonardo Franco,et al.  Layer multiplexing FPGA implementation for deep back-propagation learning , 2017, Integr. Comput. Aided Eng..

[17]  R. Karthikeyan,et al.  Multi Layer Perception Type Artificial Neural Network based Traffic Control , 2016 .

[18]  Zhong Li,et al.  A classifier of satellite signals based on the back-propagation neural network , 2015, 2015 8th International Congress on Image and Signal Processing (CISP).

[19]  Herbert Ugrinowitsch,et al.  A new training algorithm using artificial neural networks to classify gender-specific dynamic gait patterns , 2015, Computer methods in biomechanics and biomedical engineering.

[20]  Mehrorang Ghaedi,et al.  Back propagation artificial neural network and central composite design modeling of operational parameter impact for sunset yellow and azur (II) adsorption onto MWCNT and MWCNT-Pd-NPs: Isotherm and kinetic study , 2016 .

[21]  Vishnu Pratap Singh Kirar.  Improving the Performance of Back-Propagation Training Algorithm by Using ANN , 2015 .

[22]  Fatma Susilawati Mohamad,et al.  Improved Time Training with Accuracy of Batch Back Propagation Algorithm Via Dynamic Learning Rate and Dynamic Momentum Factor , 2018 .

[23]  Fatma Susilawati Mohamad,et al.  Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training , 2018 .

[24]  Rozaida Ghazali,et al.  The Effect of Adaptive Gain and Adaptive Momentum in Improving Training Time of Gradient Descent Back Propagation Algorithm on Classification Problems , 2011 .

[25]  Mansour Sheikhan,et al.  Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study , 2015, Connect. Sci..

[26]  Lixin Tang,et al.  Energy consumption prediction for steelmaking production using PSO-based BP neural network , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[27]  Isao Kojima,et al.  A Database-Hadoop Hybrid Approach to Scalable Machine Learning , 2013, 2013 IEEE International Congress on Big Data.

[28]  H. Khanna Nehemiah,et al.  Neural network classifier optimization using Differential Evolution with Global Information and Back Propagation algorithm for clinical datasets , 2016, Appl. Soft Comput..

[29]  R. Kalaivani,et al.  Neural Network based Vibration Control for Vehicle Active Suspension System , 2016 .