You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Indoor Fingerprint Positioning Based on Wi-Fi: An Overview 
Chapter Title:  (3) Artificial Neural Network 
1. Abstract of Cited Paper (BIB001):  The recent proliferation of location-based services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, wireless local area network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods 
2. Abstract of Cited Paper (BIB002):  This brief paper presents a novel localization algorithm, named discriminant-adaptive neural network (DANN), which takes the received signal strength (RSS) from the access points (APs) as inputs to infer the client position in the wireless local area network (LAN) environment. We extract the useful information into discriminative components (DCs) for network learning. The nonlinear relationship between RSS and the position is then accurately constructed by incrementally inserting the DCs and recursively updating the weightings in the network until no further improvement is required. Our localization system is developed in a real-world wireless LAN WLAN environment, where the realistic RSS measurement is collected. We implement the traditional approaches on the same test bed, including weighted k -nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and compare the results. The experimental results indicate that the proposed algorithm is much higher in accuracy compared with other examined techniques. The improvement can be attributed to that only the useful information is efficiently extracted for positioning while the redundant information is regarded as noise and discarded. Finally, the analysis shows that our network intelligently accomplishes learning while the inserted DCs provide sufficient information. 
3. Abstract of Cited Paper (BIB003):  Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions.

Literature Review:
As the principle of fingerprint data matching is similar to pattern recognition, a large number of artificial intelligence technologies used in pattern recognition can also be used in fingerprint positioning. Artificial neural network is one of the most important methods in machine learning. To date, most researchers use artificial neural network methods, such as multilayer perceptron, back propagation neural networks, multi-layer neural networks, and regression neural networks. Dai et al. BIB003 introduced the Multi-Layer Neural Network (MLNN) into indoor positioning. MLNN is a forward artificial neural network with some hidden layers existing between its input and output. The MLNN designed in reference BIB003 consisted of three parts: the transmission element, denoising element, and positioning element. The MLNN can be directly calculated from the user's location without matching fingerprint data. The experiments showed that the average error of positioning by this method was less than other mainstream methods. Fang et al. BIB001 proposed Discriminant-Adaptive Neural Networks. This method first uses multiple discriminant analysis to find useful discriminative information in the fingerprint data, and then introduces the extracted information into the discriminative components to increase the important information weights, maximizes the useful information in the neural network, and uses the information as the parameter to participate in the DANN (Discriminant-Adaptive Neural Network) calculation. Compared with other mainstream methods, this method achieved higher positioning accuracy. Battiti et al. BIB004 used a multi-layer perceptron for indoor positioning, with a positioning accuracy of 2.3 m. Nowicki et al. BIB002 proposed a method that used Deep Neural Network (DNN) combined with stacked autoencoder (SAE). The DNN used in this method was the first to be used for Wi-Fi fingerprinting and could achieve better performance in image analysis than other methods. The SAE Nowicki BIB002 used could determine the floor or building and reduce dimensionality of the input data. The experiment results were better than other networks without autoencoders.