An apparatus for controlling a system including a plurality of sources of signals, the sources causing a plurality of events, comprising: an input interface to receive signals from the sources of signals; a memory to store a neural network trained to diagnose a control state of the system, wherein the neural network includes a sequence of layers, each layer includes a set of nodes, each node of an input layer and a first hidden layer following the input layer corresponds to a source of signal in the system, wherein a pair of nodes from neighboring layers corresponding to a pair of different sources of signals are connected in the neural network only when a probability of subsequent occurrence of the events in the pair of the different sources of signals is above a threshold, such that the neural network is a partially connected neural network , wherein the probability of subsequent occurrence of the events is selected based on a frequency of such subsequent occurrence in a training data used to train the neural network; a processor to submit the signals into the neural network to produce the control state of the system; and a controller to execute a control action selected according to the control state of the system, wherein a number of nodes in the input layer equals a first multiple of a number of the sources of signals in the system, and a number of nodes in the first hidden layer following the input layer equals a second multiple of the number of the sources of signals, wherein the input layer is partially connected to the first hidden layer based on probabilities of subsequent occurrence of the events in different sources of signals, and wherein the probability of subsequent occurrence of the events of signal Si followed by events of signal Sj can be defined as P S i S j = c ij ∑ j = 1 M ∑ i = 1 M c ij − M where M is number of signals, cij is the number of times events of signal Si followed by events of signal Sj. an input interface to receive signals from the sources of signals; a memory to store a neural network trained to diagnose a control state of the system, wherein the neural network includes a sequence of layers, each layer includes a set of nodes, each node of an input layer and a first hidden layer following the input layer corresponds to a source of signal in the system, wherein a pair of nodes from neighboring layers corresponding to a pair of different sources of signals are connected in the neural network only when a probability of subsequent occurrence of the events in the pair of the different sources of signals is above a threshold, such that the neural network is a partially connected neural network , wherein the probability of subsequent occurrence of the events is selected based on a frequency of such subsequent occurrence in a training data used to train the neural network; a processor to submit the signals into the neural network to produce the control state of the system; and a controller to execute a control action selected according to the control state of the system, wherein a number of nodes in the input layer equals a first multiple of a number of the sources of signals in the system, and a number of nodes in the first hidden layer following the input layer equals a second multiple of the number of the sources of signals, wherein the input layer is partially connected to the first hidden layer based on probabilities of subsequent occurrence of the events in different sources of signals, and wherein the probability of subsequent occurrence of the events of signal Si followed by events of signal Sj can be defined as P S i S j = c ij ∑ j = 1 M ∑ i = 1 M c ij − M where M is number of signals, cij is the number of times events of signal Si followed by events of signal Sj. <EOS>