Intelligent Approaches in Locomotion - A Review <s> Jordan and Elman recurrent NN <s> The authors have developed a movement pattern generator, using an artificial neural network (ANN) for generating periodic movement trajectories. This model is based on the concept of 'central pattern generators'. Jordan's (1986) sequential network, which is capable of learning sequences of patterns, was modified and used to generate several bipedal trajectories (or gaits), coded in task space, at different frequencies. The network model successfully learned all of the trajectories presented to it. The model has many attractive properties, such as limit cycle behavior, generalization of trajectories and frequencies, phase maintenance, and fault tolerance. The movement pattern generator model is potentially applicable for improved understanding of animal locomotion and for use in legged robots and rehabilitation medicine. > <s> BIB001 </s> Intelligent Approaches in Locomotion - A Review <s> Jordan and Elman recurrent NN <s> Abstract In this paper a hierarchical control architecture for a six-legged walking machine is presented. The basic components of this architecture are neural networks which are taught in using examples of the control process. It is shown how the basic components “leg control” and “leg coordination” have been implemented by recurrent and feedforward networks respectively. The teaching process and the tests of the walking behaviour have mainly been done in a simulation system. First tests of the leg control on our real walking machine LAURON are also described. <s> BIB002
A modified Jordan network was used in BIB001 , trained using the generalized delta rule  to match reference trajectories, indexed by different inputs to the control nodes. The network is similar to a standard feed-forward network except that the outputs are fed back into a set of input neurons for the next iteration, but without self-feedback connections normally present in Jordan NNs, as these were found to hamper the ability of the system to learn periodic trajectories. Accurate fault tolerant trajectories were learned, and the system could interpolate between forms found in the training set, by varying the control inputs accordingly. Elman NNs are similar to the Jordan architectures, except that instead of recycling information from the output layer, time delayed copies of the hidden layer are maintained and fed back into the hidden layer. The Elman network was used in BIB002 and the authors claimed it had increased power (capable of a wider range of solutions) over the Jordan network. The resilient backpropagation (RPROP) training algorithm was used and the system showed an ability to learn supervised trajectories and interpolate between them.