First I'm not sure if this is the right place to post my question, but I saw some questions about ANN, and I assumed I can ask it here. I have implemented an ANN with back-propagation. I'm using it for Wi-Fi based indoor localization using fingerprinting. I saw many papers achieving accuracy below 2 m, so I hoped for such results but I didn't get it. My network is composed of input layer, 2 hidden layers, and output layer. The input layer has 14 neurons, each hidden layer has 8 neurons, and the output layer has 1 neuron. My sample space that I used to train the network is composed of 630 sample.
I trained the network using different configurations for the learning rate and training iterations. I didn't achieve any good results at all. The average accuracy I achieved was 24 m, which is really bad.
My questions: 1. How can I verify whether the problem is in my code or my ANN design? 2. How to improve on the design to get better results? 3. How to choose which learning rate to start with and how many iterations for training?
This is my first time dealing with ANN, and I didn't find any clear guidelines of how to design it.