In the context of human-robot and robot-robot interactions, the better cooperation can be achieved by predicting the other party’s subsequent actions based on the current action of the other party. The time duration for adjustment is not sufficient provided by short term forecasting models to robots. A longer duration can by achieved by mid-term forecasting. But the mid-term forecasting models introduce the previous errors into the follow-up forecasting and amplified gradually, eventually invalidating the forecasting. A new mid-term forecasting with error suppression based on restricted Boltzmann machine(RBM) is proposed in this paper. The proposed model can suppress the error amplification by replacing the previous inputs with their features, which are retrieved by a deep belief network(DBN). Furthermore, a new mechanism is proposed to decide whether the forecasting result is accepted or not. The model is evaluated with several datasets. The reported experiments demonstrate the superior performance of the proposed model compared to the state-of-the-art approaches.
X-ray microanalysis,convergent beam electron diffraction(CBD)and selected area electrondiffraction(SAD)studies on the structures and compositions of the constituent phases in2024 series Al alloys have been conducted.Partial substitution of alloying elements is found tooccur in all the constituent phases,which cause small deviations from the stoichiometric com-positions reported in these ternary compounds.The dominant phase is α-Al_(12)(FeMn)_3Si whichhas a body center cubic crystal structure with the Im■ space group and a=1.25 nm.The nextdominant phase is Cu_2FeAl_7 which has a primitive tetragonal crystal structure with theP4/mnc space group and a=0.6336 nm,c=1.487 nm.The minor phase is α'-Al_(12)Fe_3Si hav-ing α primitive cubic crystal structure with the Pm■ space group and α=1.27 nm. 相似文献
