Linear self-associative memories are a type of simple and efficient neural network. They consist of a single layer of linearly interconnected units that operate simultaneously. A memory is considered self-associative when it matches each input element to itself. The primary purpose of linear self-associative memories is to store a set of data in memory and retrieve that stored data when presented with incomplete or corrupted versions.