An improved stochastic configuration network for concentration prediction in wastewater treatment process. (English) Zbl 1536.93851
Summary: A learner model with fast learning and compact architecture is expected for industrial data modeling. To achieve these goals during stochastic configuration networks (SCNs) construction, we propose an improved version of SCNs in this paper. Unlike the original SCNs, the improved one employs a new inequality constraint in the construction process. In addition, to speed up the construction efficiency of SCNs, a node selection method is proposed to adaptively select nodes from a candidate pool. Moreover, to reduce the redundant nodes of the built SCNs model, we further compress the model based on the singular value decomposition algorithm. The improved SCNs are compared with other methods over four datasets and then applied to the ammonia-nitrogen concentration prediction task in the wastewater treatment process. Experimental results indicate that the proposed method has good potential for industrial data analytics.
MSC:
| 93E03 | Stochastic systems in control theory (general) |
| 93B70 | Networked control |
| 68T07 | Artificial neural networks and deep learning |
Keywords:
stochastic configuration networks; incremental learning; randomized neural networks; wastewater treatment processReferences:
| [1] | Castellano, G.; Fanelli, A. M.; Pelillo, M., An iterative pruning algorithm for feedforward neural networks, IEEE Trans. Neural Netw., 8, 3, 519-531 (1997) |
| [2] | Dai, W.; Li, D.; Zhou, P.; Chai, T., Stochastic configuration networks with block increments for data modeling in process industries, Inf. Sci., 484, 367-386 (2019) |
| [3] | Dai, W.; Zhou, X.; Li, D.; Zhu, S.; Wang, X., Hybrid parallel stochastic configuration networks for industrial data analytics, IEEE Trans. Ind. Inform., 18, 4, 2331-2341 (2022) |
| [4] | Dürrenmatt, D. J.; Gujer, W., Data-driven modeling approaches to support wastewater treatment plant operation, Environ. Model. Softw., 30, 47-56 (2012) |
| [5] | Gorban, A. N.; Tyukin, I. Y.; Prokhorov, D. V.; Sofeikov, K. I., Approximation with random bases: proet contra, Inf. Sci., 364-365, 129-145 (2016) · Zbl 1427.68361 |
| [6] | Hunter, D.; Yu, H.; Pukish, M. S.; Kolbusz, J.; Wilamowski, B. M., Selection of proper neural network sizes and architectures—a comparative study, IEEE Trans. Ind. Inform., 8, 228-240 (2012) |
| [7] | Kwok, T. Y.; Yeung, D. Y., Constructive algorithms for structure learning in feedforward neural networks for regression problems, IEEE Trans. Neural Netw., 8, 630-645 (1997) |
| [8] | Li, M.; Huang, C.; Wang, D., Robust stochastic configuration networks with maximum correntropy criterion for uncertain data regression, Inf. Sci., 473, 73-86 (2019) · Zbl 1450.62123 |
| [9] | Li, M.; Wang, D., Insights into randomized algorithms for neural networks: practical issues and common pitfalls, Inf. Sci., 382-383, 170-178 (2017) · Zbl 1429.68253 |
| [10] | Li, M.; Wang, D., 2-D stochastic configuration networks for image data analytics, IEEE Trans. Cybern., 51, 1, 359-372 (2021) |
| [11] | Lu, J.; Ding, J., Construction of prediction intervals for carbon residual of crude oil based on deep stochastic configuration networks, Inf. Sci., 486, 119-132 (2019) |
| [12] | Lu, J.; Ding, J.; Dai, X.; Chai, T., Ensemble stochastic configuration networks for estimating prediction intervals: A simultaneous robust training algorithm and its application, IEEE Trans. Neural Netw. Learn. Syst., 31, 5426-5440 (2020) |
| [13] | Pao, Y.-H.; Park, G.-H.; Sobajic, D. J., Learning and generalization characteristics of the random vector functional-link net, Neurocomputing, 6, 2, 163-180 (1994) |
| [14] | Pao, Y. H.; Takefuji, Y., Functional-link net computing: theory, system architecture, and functionalities, Computer., 25, 76-79 (1992) |
| [15] | Qiao, J.-F.; Guo, X.; Li, W.-J., An online self-organizing algorithm for feedforward neural network, Neural Comput. Appl., 32, 23, 17505-17518 (2020) |
| [16] | Qiao, J.; Zhang, W., Dynamic multi-objective optimization control for wastewater treatment process, Neural Comput. Appl., 29, 11, 1261-1271 (2018) |
| [17] | Qiao, J.-F.; Han, H.-G., Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach, Automatica, 48, 8, 1729-1734 (2012) · Zbl 1267.93175 |
| [18] | Reed, R., Pruning algorithms-a survey, IEEE Trans. Neural Netw., 4, 740-747 (1993) |
| [19] | Scardapane, S.; Wang, D., Randomness in neural networks: an overview, Wiley Interdiscip, Rev. Data Min. Knowl. Discov., 7, 2, e1200 (2017) |
| [20] | W.F. Schmidt, M.A. Kraaijveld, R.P.W. Duin, Feedforward neural networks with random weights, in: Proc. 11th IAPR Int. Conf. Pattern Recognit. Methodol. Syst., IEEE Comput. Soc. Press, 1992, pp. 1-4. |
| [21] | Sharma, S.; Chandra, P., Constructive neural networks: A review, Int. J. Eng. Sci. Technol., 2, 7847-7855 (2010) |
| [22] | Kwok, T. Y.; Yeung, D. Y., Objective functions for training new hidden units in constructive neural networks, IEEE Trans. Neural Netw., 8, 1131-1148 (1997) |
| [23] | I. Tyukin, D. Prokhorov, Feasibility of random basis function approximators for modeling and control, in Proc. IEEE Int. Conf. Control Appl. Intell. Control, St. Petersburg, Russia, 2009, pp. 1391-1396. |
| [24] | Wang, D., Editorial: Randomized algorithms for training neural networks, Inf. Sci., 364-365, 126-128 (2016) · Zbl 1427.68017 |
| [25] | Wang, D.; Cui, C., Stochastic configuration networks ensemble with heterogeneous features for large-scale data analytics, Inf. Sci., 417, 55-71 (2017) |
| [26] | Wang, D.; Li, M., Stochastic configuration networks: Fundamentals and algorithms, IEEE Trans. Cybern., 47, 10, 3466-3479 (2017) |
| [27] | D. Wang, M. Li, Deep stochastic configuration networks with universal approximation property, in: 2018 Int. Jt. Conf. Neural Netw. IJCNN, IEEE, Rio de Janeiro, 2018, pp. 1-8. |
| [28] | Wang, W.; Wang, D., Prediction of component concentrations in sodium aluminate liquor using stochastic configuration networks, Neural Comput. Appl., 32, 17, 13625-13638 (2020) |
| [29] | Xie, J.; Zhou, P., Robust stochastic configuration network multi-output modeling of molten iron quality in blast furnace ironmaking, Neurocomputing., 387, 139-149 (2020) |
| [30] | Zhu, X.; Feng, X.; Wang, W.; Jia, X.; He, R., A further study on the inequality constraints in stochastic configuration networks, Inf. Sci., 487, 77-83 (2019) · Zbl 1451.68238 |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
