Implementing perceptrons by means of water-based computing

Abstract

Water-based computing emerged as a branch of membrane computing in which water tanks act as permeable membranes connected via pipes. Valves residing at the pipes control the flow of water in terms of processing rules. Resulting water tank systems provide a promising platform for exploration and for case studies of information processing by flow of liquid media like water. We first discuss the possibility of realizing a single layer neural network using tanks and pipes systems. Moreover, we discuss the possibility to create a multi-layer neural network, which could be used to solve more complex problems. Two different implementations are considered: in a first solution, the weight values of the connections between the network nodes are represented by tanks. This means that the network diagram includes multiplication structures between the weight tanks and the input tanks. The second solution aims at simplifying the network proposed in the previous implementation, by considering the possibility to modify the weight values associated to neuron by varying the diameter of the connecting pipes between the tanks. The multiplication structures are replaced with a timer that regulates the opening of the outlet valves of all the tanks. These two implementations can be compared to evaluate their efficiency, and considerations will be made regarding the simplicity of implementation.