# FnmOS-ELM: A Flexible Neural Network Mixed Online Sequential Elm
## 1 Introduction  
Classification tasks on various datasets have become a hot topic over the past decades. The accuracy of classification depends on two aspects: feature representation and classifier's discriminability. Convolutional neural network (CNNs) [1] and many other network models are derived for feature extraction, which can directly take image data as input with their unique fine-grained feature extraction method without manual image preprocessing and other additional complex operations [2]. Recurrent neural networks (RNNs) [3] can remember the previous information and have more advantages over other network models in continuous, context-related, and feature extraction-related tasks, such as speech recognition. Similar to CNNs and RNNs, other types of neural networks have their own advantages in feature extraction, and great achievements have been made in recent studies [4; 5; 6; 7]. In terms of classification, full connection layers play a major role in CNNs- or RNNs-based classifiers that use the back-propagation (BP) [8] algorithm to train networks. Previous studies have shown that the BP method is very sensitive to local minima, and overmuch training could lead to a decline in generalizability [9]. In addition, the repeated adjustment of the learning rate during the training process causes efficiency issues in research. The extreme learning machine (ELM) method [10; 11; 12; 13; 14] has been proven to be a fast and effective classification algorithm, which can be used to train multi-hidden layers feed-forward neural networks. Each hidden layer can be trained by asingle-layer ELM. The whole network can be regarded as a single-layer ELM without adjusting the hidden layer nodes used for clustering, regression, and classification. The online sequential ELM (OS-ELM) [15], a fast and accurate online sequential learning algorithm, is suitable for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) [11] hidden nodes in a unified framework. The OS-ELM is superior to other sequential learning algorithms and exhibits a superior generalizability with a relatively small number of training steps in real-world benchmark regression, classification, and time-series problems. With the wide application of deep learning [16; 17], the datasets are becoming increasingly more complex, which not only requires the classifier to have a good ability to distinguish and extract features, but also requires the algorithm to classify quickly and accurately. Although CNNs and RNNs have excellent feature representation, when the computing resources are limited, they take too long time for training model to achieve accurate classification [18; 19; 20]. On the other hand, the OS-ELM has high training and classification speed yet poor feature representation. Therefore, we propose a flexible neural networks mixed OS-ELM (fnnmOS-ELM) in this paper, which uses CNNs or RNNs as feature extractors and employs the OS-ELM as a classifier. Undoubtedly, when to tackle more complex CNNs, optimizing the original network structure could be more suitable than modifying it. Then we propose a method for using fnnmOS-ELM as an optimiser. In short, we propose a fnnmOS-ELM by fully combining the advantages of CNNs, RNNs, and OS-ELM. The main intellectual contributions can be summarized as follows. 1. The proposed fnnmOS-ELM fully exploits the feature representation of CNNs and RNNs with different datasets and makes use of the excellent classification characteristics of OS-ELM. 2. We extend the application of OS-ELM to more datasets, and our studies also show that fnnmOS-ELM can optimize the network performance of CNNs or RNNs without changing the original network structure. 3. We explore the effects of various hyper-parameters on the performance of the model in the mixed structure in detail and explain how to improve the performance of the model by adjusting these parameters. The remainder of this paper is organized as follows. Section 2 gives an overview of the related work.