Pecific information forms to ascertain functions between the process mean or variance and input factors. Over the past two decades, artificial neural networks (ANNs), usually called neural networks (NNs), have already been broadly employed to classify, cluster, approximate, forecast, and optimize datasets within the fields of biology, medicine, industrial engineering, manage engineering, software engineering, environmental science, economics, and sociology. An ANN can be a quantitative numerical model that originates from the organization and operation on the neural networks of the biological brain. The basic creating blocks of every single ANN are artificial neurons, i.e., basic mathematical models (functions). Typical ANNs comprise thousands or millions of artificial neurons (i.e., nonlinear processing units) connected via (synaptic) weights. ANNs can “learn” a task by adjusting these weights. Neurons receive inputs with their associated weights, transform these inputs working with activation functions, and pass the transformed information and facts as outputs. It has been theoretically proved that ANNs can approximate any continuous mapping to arbitrary precision without having any assumptions [192]. Furthermore, devoid of any knowledge of underlying principles, ANNs can determine unknown interactions between the input and output performances of a course of action since of their data-driven and self-adaptive properties. Accordingly, the functional correlation between the input and output good quality characteristics in RD may be modeled and analyzed by NNs without any assumptions. The integration of an NN into the experiment design procedure of an RD model has been talked about in Rowlands et al. [23] and Shin et al. [24]. In current times, Arungpadang and Kim [25] presented a feed-forward NN-based RSM that improved the precision of estimations without the need of added experiments. Le et al. [26] proposed an NN-based estimation technique that identified a brand new screening process to determine the optimum transfer function, in order that a more precise option could be obtained. A genetic algorithm with NNs has been executed in Su and Hsieh [27], Cook et al. [28], Chow et al. [29], Chang [30], Chang and Chen [31], Arungpadang et al. [32], and Villa-Murillo et al. [33] as an estimation technique to investigate the optimal quality characteristics with related control issue settings in the RD model with no the usage of estimation formulas. Winiczenko et al. [34] introduced an effective optimization process by combining the RSM in addition to a genetic algorithm (GA) to locate the optimal topology of ANNs for predicting colour adjustments in rehydrated apple cubes.Appl. Sci. 2021, 11, x FOR PEER REVIEW3 ofAppl. Sci. 2021, 11,handle factor settings inside the RD model with no the usage of estimation formulas. 3 of 18 Winiczenko et al. [34] introduced an effective optimization process by combining the RSM in addition to a genetic algorithm (GA) to discover the optimal topology of ANNs for predicting color modifications in rehydrated apple cubes. As a result, the primary objective is usually to propose a new dual-response estimation approach Consequently,primarily based on NNs. First, theto propose a new procedure mean and normal deviation functions the primary objective is o-Phenanthroline medchemexpress standard quadratic dual-response estimation approach primarily based on NNs. in RD the typical quadratic procedure mean and common deviation functions technique. Initial, are estimated employing the proposed functional-link-NN-based estimation in RD are estimated working with the proposed functional-link-NN-based estimation technique. SecSecond, the Bayesian informat.
