## 2 Literature Review  
In the previous section, we highlighted the problem of emphasized transfer learning. The transfer learning process involves transferring knowledge from one person to another, and is normally conducted between two domains--one that can easily collect data, while the other faces difficulty collecting data [12]. However, transfer learning is valuable when performed in the same domain and a new learner is required, while knowledge is transferred to the previous learner.  
In Ref. [13], the authors employed an online approach to quickly adapt the \"black box\" classifier for the new test data set, without keeping the classifier or evaluating the original optimization criterion. A continuous number was represented by a threshold, which determines the class by considering the original classifier outputs. In addition, points near the original boundary are reclassified by employing a Gaussian process regression scheme. In the context of a classifier cascade, this general procedure that showed performance surpassing state-of-the-art results in face detection based on a standard data set can be employed.  
The study presented in Ref. [9] focused on transfer learning for the extreme learning machine (ELM), which put forward a FA-OSELM algorithm allowing the original model's transfer to a new one by employing few data having new features. This approach evaluates the new model's suitability for the new feature dimensions. These experiments showed that the FA-OSELM is highly accurate by employing even a small amount of new data, and is considered an efficient approach that allows practical lifelong indoor localization. Transfer learning integration was done in various fields, such as sensing, computer vision, and estimation. However, these works did not focus on enhancing the classifier's prediction in online mode by considering the learned classifier from the previous block of data. The cyclic dynamic data are a useful application for such a problem, in which previous knowledge can be employed to forecast coming classes due to the fact that cycles occur in sequential data.  
The work conducted in Ref. [14] describes a novel type of extreme learning machine with the capability of preserving older knowledge, using external memory and transfer learning; ITM-OSELM. In this study, the authors applied the concept to Wi-Fi localization and showed good performance improvement in the context of cyclic dynamic and feature adaptability of Wi-Fi navigation. However, the approach has not been generalized to other cyclic dynamic scenarios in the machine learning field, and its applicability has not been verified in various types of cyclic dynamics.  
All the reviewed articles have aimed at providing their models with some type of transfer learning; however, taking the concept of transfer learning as a need for incremental learning based approaches and enabling restoration of knowledge gained from older chunks of data in the scenarios of cyclic dynamic has not been tackled explicitly in the literature. There is a need for such models in various real life applications like Wi-Fi navigation where people visits previously visited places frequently, also in intrusion detection systems when newer attacks follows behavior similar to older attacks with addition of new features, etc.  
The goal of this article is to develop a generalized ITM-OSELM model and to build a simulator for cyclic dynamic. We then used our model to both validate and evaluate ITM-OSELM performance in feature adaptive and cyclic dynamic situations.