A radial basis function neural network-based 2-satisfiability reverse analysis (RBFNN-2SATRA) primarily depends on adequately obtaining the linear optimal output weights, alongside the lowest iteration error. This study aims to investigate the effectiveness, as well as the capability of the artificial immune system (AIS) algorithm in RBFNN-2SATRA. Moreover, it aims to improve the output linearity to obtain the optimal output weights. In this paper, the artificial immune system (AIS) algorithm will be introduced and implemented to enhance the effectiveness of the connection weights throughout the RBFNN-2SATRA training. To prove that the introduced method functions efficiently, five well-established datasets were solved. Moreover, the use of AIS for the RBFNN-2SATRA training is compared with the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. In terms of measurements and accuracy, the simulation results showed that the proposed method outperformed in the terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). The introduced method outperformed the existing four algorithms in the aspect of robustness, accuracy, and sensitivity throughout the simulation process. Therefore, it has been proven that the proposed AIS algorithm effectively conformed to the RBFNN-2SATRA in relation to (or in terms of) the average value of training of RMSE rose up to 97.5%, SBC rose up to 99.9%, and CPU time by 99.8%. Moreover, the average value of testing in MAE was rose up to 78.5%, MAPE—rose up to 71.4%, and was capable of classifying a higher percentage (81.6%) of the test samples compared with the results for the GA, DE, PSO, and ABC algorithms.

中文翻译：

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基于径向基函数神经网络的基于2-满意度的逆向分析方法的人工免疫系统

基于径向基函数神经网络的2满意度反向分析（RBFNN-2SATRA）主要取决于充分获得线性最优输出权重以及最低的迭代误差。本研究旨在研究RBFNN-2SATRA中人工免疫系统（AIS）算法的有效性和功能。此外，其目的在于改善输出线性度以获得最佳输出权重。在本文中，将引入并实施人工免疫系统（AIS）算法，以提高整个RBFNN-2SATRA训练中连接权重的有效性。为了证明所介绍的方法有效地起作用，解决了五个公认的数据集。此外，将AIS在RBFNN-2SATRA训练中的使用与遗传算法（GA），差分进化（DE），粒子群优化（PSO）和人工蜂群（ABC）算法。在测量和精度方面，仿真结果表明，该方法在平均绝对误差（MAE），平均绝对百分比误差（MAPE），均方根误差（RMSE），施瓦兹贝叶斯准则（SBC），和中央处理器时间（CPU时间）。在整个仿真过程中，引入的方法在鲁棒性，准确性和灵敏度方面都优于现有的四种算法。因此，已经证明，相对于（或就其而言），RMSE的训练平均值上升至97.5％，SBC上升至99.9％，并且CPU时间相对于RBFNN-2SATRA有效地符合RBFNN-2SATRA。增长了99.8％。此外，MAE中的测试平均值上升至78.5％，MAPE-上升至71.4％，